Original Research Article - Onkologia i Radioterapia ( 2024) Volume 0, Issue 0

# Utilizing support vector machine algorithm and feature reduction for accurate breast cancer detection: An exploration of normalization and hyper parameter tuning techniques

V Shiva Kumar Chary^{*}, Bellamkonda Satya Sai Venkateswarlu, Saketh Vemuri, Venkata Naga Sai Suraj Pasupuleti, Vijaya Babu Burra and Praveen Tumuluru

**V Shiva Kumar Chary, Department of Computer science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India,**

^{*}Corresponding Author:**Email:**valabojushivakumarchary789@gmail.com

**Received: **22-Nov-2023, Manuscript No. OAR-23-120739;
, Pre QC No. OAR-23-120739(PQ);
**Editor assigned: **24-Nov-2023, Pre QC No. OAR-23-120739(PQ);
**Reviewed: **08-Dec-2023, QC No. OAR-23-120739;
**Revised: **15-Dec-2023, Manuscript No. OAR-23-120739 (R);
**Published:**
25-Dec-2023

## Abstract

OIn this work, we will evaluate the impact of Independent Component Analysis (ICA) on a breast cancer decision support system’s feature reduction capabilities. The Wisconsin Diagnostic Breast Cancer (WDBC) dataset will be utilised to construct a one-dimensional feature vector (IC). We will study the performance of k-NN, ANN, RBFNN, and SVM classifiers in spotting mistakes using the original 30 features. Additionally, we will compare the IC-recommended classification with the original feature set using multiple validation and division approaches. The classifiers will be tested based on specificity, sensitivity, accuracy, F-score, Youden’s index, discriminant power, and Receiver Operating Characteristic (ROC) curve. This effort attempts to boost the medical decision support system’s efficiency while minimising computational complexity.

#### Keywords

Independent Component Analysis (ICA), breast cancer decision support system, feature reduction, Wisconsin Diagnostic Breast Cancer (WDBC) dataset, one-dimensional feature vector, original 30 features, ic-recommended classification, validation, division approaches, specificity, sensitivity, accuracy, f-score, youden’s index, discriminant power, Receiver Operating Characteristic (ROC) curve, medical decision support system, computational complexity, efficiency improvement

#### Introduction

Breast cancer is a leading cause of mortality among women [1], underlining the significance of effective diagnostics for early detection and treatment. However, present diagnostic procedures largely rely on the competency of physicians and physical tests, leaving them prone to errors [2]. As people may cope with ambiguous assessments, automated breast cancer screening applying machine learning has developed as a practical technique to enhance diagnostic accuracy [3-5]. A study comparing machine learning with human analysis found that machine learning obtained an accuracy of 91.1%, topping even the highly trained physicians at 79.97% [6].

Breast tumours are categorised as benign or malignant, with malignant tumors presenting a serious hazard to life [7]. Artificial Neural Networks (ANN) and Radial Basis Function Neural Networks (RBFNN) have proved helpful in identifying benign and aggressive tumours [8-10]. RBFNN stands out for its easiness, rapid convergence, and competence in pattern recognition [10,11]. However, its performance may diminish as the input dimension expands, due to processing issues [12]. In contrast, Support Vector Machine (SVM) has appeared as a promising tool for data categorization, particularly in high-dimensional feature spaces [13].

To manage the difficulties produced by high-dimensional datasets, dimensionality reduction methods like Principal Component Analysis (PCA) and Independent Component Analysis (ICA) have been utilised [14,15]. ICA, applying higher order statistics, isolates independent components with more information than PCA, minimising data dimensionality while boosting classifier performance and convergence time [12,16,17].

This research focused on studying how ICA-based feature reduction influences breast cancer categorization as benign or aggressive. The WDBC dataset is transformed into a one-dimensional feature vector using ICA. k-NN, ANN, RBFNN, and SVM models are trained and graded using 5/10-fold cross-validation and 20% splitting. Performance metrics, including accuracy, specificity, sensitivity, F-score, Youden’s index, and discriminant power, are generated and illustrated using Receiver Operating Characteristic (ROC) graphs to compare the models.

The study’s technique contains an introduction of the dataset, ICA, k-NN, ANN, RBFNN, SVM, and performance measures in Part 2. Section 3 outlines the approach adopted. Sections 4 to 5 present and appraise the experiment’s results. The investigation finishes in Section 6.

In conclusion, the research seeks to explain opinions on the utility of ICA in decreasing feature dimensions for breast cancer classification. By blending machine learning with dimensionality reduction approaches, this effort seeks to boost early detection and diagnostic accuracy, ultimately leading to better breast cancer outcomes.

#### Materials and Methods

**Data overview**

The WDBC collection consists of 569 samples, with 357 being
normal and 212 malignant. Each sample is tagged with a unique
number, a description (B=normal, M=malignancy), and 30 characteristics. **Figure 1 **depicts features extracted from a digitally enhanced
breast mass Fine Needle Aspirate (FNA) picture.

**Figure 1:** Breast Fine-Needle Aspiration (FNA) biopsies can diagnose both malignant
(a) and benign (b) breast cancers [24].

The mean, standard error, and “worst” values of 10 properties were computed for each cell nucleus, resulting in 30 features [18].

**Blind source separation**

e.g., In the ICA model, the observed signal is a linear combination of two independently distributed sources.

*x=As*

In the ICA framework, the observed signal vector x is regarded to
be a linear mixture of two independent source signals represented
by vector s, and the mixing matrix A holds the unknown mixing
coefficients. After estimating A *via* ICA, the separation matrix W is
generated as the inverse of A. By applying W to the detected signal,
one may obtain the original source signals.

To construct Independent Components (ICs), the data is centered
by subtracting the mean values of variables (similar to PCA). The
next step is whitening, which makes the data uncorrelated and each
variable has a variance of one. Unlike PCA, which gives eigenvectors
for decorrelated data, ICs are obtained *via* a linear transformation
on the uncorrelated data **Table 1.**

SNO | 10 Real-world advantages |
---|---|

1 | Radius (Mean of distances from center to points on the perimeter) |

2 | Texture ( Standard deviation of grey-scale values) |

3 | Perimeter |

4 | Area |

5 | Smoothness(local variation in radius lengths) |

6 | Compactness (perimeter2/area-1.0) |

7 | Concavity(severity of concave portions of the contour) |

8 | Concave points(number of concave portions of the contour) |

9 | Symmetry |

10 | Fractal dimension ("coastline approximation"-1) |

**Tab. 1. ** For every cell nucleus, a set of numerical attributes has been calculated.

*ic _{i}=b^{T}_{i}x*

To construct Independent Components (ICs), the data is centered
by subtracting the mean values of variables (similar to PCA). The
next step is whitening, which makes the data uncorrelated and each
variable has a variance of one. Unlike PCA, which gives eigenvectors
for decorrelated data, ICs are obtained via a linear transformation
on the uncorrelated data **Table 1**.

In this study, the Independent Component (IC) is denoted as “ic,” and the vector necessary to construct it is marked as “b.” To compute the vector “b,” a variable independence-linked objective function is applied. The FASTICA approach, known for its adaptability and interactive qualities, is utilised to compute the ICs in this work [19].

**Deep learning networks**

The Feedforward Neural Network (FFNN) is often applied in numerous applications thanks to its simplicity of mathematical analysis and good representation capabilities [20,21]. It has demonstrated to be effective in control, signal processing, and pattern recognition applications.

In **Figure 2**, the FFNN architecture is depicted, with N showing
the total number of input patterns and M representing the total
number of neurons in the hidden layer. Neurons in the hidden
layer examine weighted inputs from the previous layer and pass
outputs to neurons in the next layer in the FFNN architecture.

**Figure 2:** The structural design of a feedforward neural network.

In the Feedforward Neural Network (FFNN), the system output, known as yout, is created using a nonlinear activation function f(ynet). The output ynet is the sum of weighted inputs from the input neurons, where wi specifies the weight of each input neuron xi. Additionally, w0 represents the bias term. The observed neural network output value is represented as yobs. The error between the network result and the output value is represented as E [22].

A Radial Basis Function Neural Network (RBFNN) consists of three layers, like a feedforward architecture, however the hidden layer is known as the radial basis layer and largely uses Gaussian functions. Each neuron in the buried layer is characterised by a Radial Basis Function (RBF) centered on a given location. During training, the centers and dispersion of these RBFs are calculated. To compute the output, a hidden neuron calculates the Euclidean distance between the input vector and the center point of the RBF, and then applies the RBF kernel function based on the distance using the spread value. This method allows the RBFNN to learn and articulate intricate patterns in the data [23-27].

**Kernel machine (SVM)**

SVM, a supervised learning method, is used for data regression and classification. It was proposed by Boser and Vapnik [28,29]. The SVM technique tries to generate a hyperplane that can differentiate classes with minimal training error and maximum margin, enhancing its generalization ability.

For datasets that can be linearly partitioned, a linear SVM approach
is suitable. The objective is to maximize the distance between classes.
Support vectors are the data points on the edges of the margin,
illustrated by the dotted lines in **Figure 3**.

**Figure 3:** A separating boundary in support vector machine.

The discriminant function for the hyperplane, denoted as g(x), is given by

Where w represents the coefficient vector, b is the offset from the origin, and x denotes the data points. For a data point on one class, g(x) equals 0, while for the nearest point on the other class, g(x) equals 0. The support vectors, located on the separating hyperplane, areee cost function should be minimized while maximizing

This is a quadratic optimization problem with linear inequality constraints. The Lagrange function is identified using the Karush- Kuhn-Tucker (KKT) criterion [30].

To attain the ideal values of w and b, where I are lagrange multipliers, Lp must be decreased. The following is the optimization equation:

Indeed, the kernel technique is a powerful strategy that allows SVM to solve nonlinear classification challenges. By utilising a kernel function, the data points are transported to a higher-dimensional space (denoted by Φ(X)), where a linear hyperplane may separate the classes. The new discriminant function, g(x), is expressed as

Where W denotes the coefficient vector in the higher-dimensional
space, and b is the offset from the origin. This method, SVM may
efficiently classify data that may not be linearly separable in the
original feature space **Table 2**.

Actual value/ Recognized value | Positive | Negative |
---|---|---|

Positive | TP | FN |

Negative | FP | TN |

**Tab. 2.** A contingency table used for evaluating the performance of a Two-class classification model.

The optimization equation combines mapping input vectors to kernel
space X with Φ(*X*).

The kernel function K(xi, xj) includes the values Φ(xi) and Φ(xj), where Φ denotes the mapping to kernel space. Common kernel functions include RBFs and polynomials.

**Performance measures**

Various techniques exist to examine a classifier’s effectiveness. The confusion matrix illustrates correct and inaccurate classifications, with TP, TN, FP, and FN denoting different findings. Classifier accuracy, typically utilised, is calculated as

displaying the proportion of correct projections. Sensitivity (TPR) shows the classifier’s capacity to distinguish positive examples, while specificity (TNR) examines its skill to recognise negative instances. F-score combines accuracy and recall, whereas Youden’s index analyses both sensitivity and specificity [31].Receiver Operating Characteristic (ROC) curves analyse performance over several thresholds, and the Area Under the Curve (AUC) represents overall performance.

Sensitivity checks the proper identification of actual positives, whereas specificity rates the detection of true negatives.

The F-score analyses measure accuracy by balancing precision and recall. It tests the classifier’s accuracy and memory performance. When the F-score is 1, accuracy and recall are equally relevant. If it’s 1, recall is emphasised, and if it’s 2, accuracy is prioritised.

Where is the bias and *β = 1 *is the F-Score. When the value is 1, recall is valued; when the value
is 2, precision is sought.

Discriminant Power (DP) and Youden’s index are two more metrics used to analyze a classifier’s potential to make medical diagnoses. DP tests a classifier’s ability to distinguish between positive and negative samples:

The eventual consequence may be expressed clearly as follows: In the DPs indicated below, the phrases “poor discriminant,” “limited discriminant,” “fair discriminant,” and “great discriminant” are used: 1, 2, and 3.

This study investigates classifier efficacy using Youden’s index and the Receiver Operating Characteristic (ROC) curve. The research contains 5/10-fold Cross-Validation (CV) with 20% data partitioning. CV splits data into subsets for training and testing, assessing the model’s discriminating performance after repeated rounds. Data splitting randomly chooses 20% for testing and employs the remaining for training, being less accurate but easier to perform [32,33].

**Literature survey**

The domain of medical diagnostics has been considerably changed by the deployment of machine learning and deep learning technology. Several recent research publications have given unique approaches for early disease diagnosis and proper rating in various medical scenarios. For instance, Mohamed created a technique merging convolutional neural networks with two-dimensional discrete wavelet transform to recognise seizures [1]. Agudelo Gaviria and Sarria-Paja described a breast cancer detection strategy applying deep learning models based on digital diagnostic pictures [2]. Amer mdeveloped a technique for identifying lung lesions in CT images using feature integration and a genetic algorithm [3].

Hesham created an ensemble learning-based technique for proper breast cancer labelling [4]. In a similar experiment, Yang constructed an astounding multiple heartbeats categorization model based on convolutional neural networks and bidirectional extended short-term memory [5]. Sumana established an artificial neural network- based technique for identifying nephrolithiasis with KUB ultrasound imaging [6]. Whereas Gunasundari designed a deep convolutional neural network for locating liver lesions with abdominal CT data [7].

Mert proposed a reduced feature set-based model for breast cancer diagnosis [8]. Zhang explored a computer-aided diagnostic approach for breast focal imbalance [9]. Rao made a comparison examination of flaw detection in distribution systems utilising DWT-FFNN and DWT-RBFNN [10]. Thakur created a model for face recognition using posterior distance model-based radial basis function neural networks [11].

Hashim and Alzubaydi established a technique for discerning secret information based on lowered coefficient values of 2DHWT sub- bands [12]. Adnan established an explicit AI-based methodology for monitoring student progress in virtual learning environments [13]. Haruna presented a neuro-genetic model for forecasting crude oil prices [14]. Wanga created a complete regression neural network-based model for assessing chip probe output [15]. Jothikumar designed a more effective remote application monitoring solution leveraging the PROXMOX virtual environment [16].

Finally, Bazatbekov created a 2D face recognition model combining PCA and triplet similarity embedding [17]. These findings highlight the great potential of machine learning and deep learning technology across several fields, including medical monitoring. These occurrences establish a good foundation for further investigation in this area,

**Methodology**

This research analyses classifier performance on breast cancer data
with both the original 30 features and a single feature reduced
through ICA. **Figure 4** depicts the model applied to WDBC data
with 30 attributes, trained and assessed on 569 occurrences (patients).

**Figure 4:** The fundamental framework of the research.

In this study, we apply ICA to lower the data complexity, and then
split the data into subgroups using 5/10-CV and 20% partitioning.
ANN, RBFNN, SVM, and k-NN models are taught and assessed
following these subgroups. Performance measures including sensitivity,
specificity, accuracy, F-score, Youden’s index, DP, and ROC
curve are produced from the classification data. The first IC, adopted
as a feature vector owing of its high eigenvalue, adequately
encapsulates the information of the thirty qualities. **Figure 5** depicts
this selection technique. Additionally, **Figure 6 **displays the IC’s distribution,
revealing its discriminative features. The diagnostic performance
of the algorithms is examined on the test data to show its
utility in breast cancer classification

**Figure 5:** The eigenvalues that correspond to the WDBC dataset.

**Figure 6:** The estimated IC’s probability density function (feature vector after
dimensionality reduction).

The k-NN algorithm leverages the one-dimensional Euclidean distance (d=(xtest - xtraining)^2) between test and training data. Performance statistics are retained for the greatest k value, acquired from k values ranging from 1 to 25. The ANN model contains a feedforward neural network with one hidden layer, continuously increasing the number neurons in the hidden layer for best accuracy. The log-sigmoid transfer function is employed for the hidden layer’s activation function, and the network is taught using the gradient descent with momentum and variable learning rate backpropagation technique. Additionally, RBFNN is tested with varying spread value. SVM techniques evaluate linear, quadratic, and RBF kernels for successful breast cancer categorization.

#### Results

In this study, we utilised the WDBC data to train and evaluate
models leveraging a one-dimensional feature vector produced using
ICA. We examined accuracy, sensitivity, and specificity for
one-dimensionality using a 5/10 CV technique with 20% test data.
Sensitivity was stressed because it is crucial in detecting dangerous
cancers. The accuracy of the k-NN classifier was tested for k values
ranging from 1 to 25, and **Figure 7 **shows a comparison graph illustrating
the effect of ICA on the k-NN classifier’s performance. The
findings give vital information on the efficiency of feature reduction
using ICA for breast cancer classification.

**Figure 7:** The estimated IC’s probability density function (feature vector after
dimensionality reduction). 10-fold CV with 30 features; 10- fold CV with 1 feature.

The study investigated k-NN, ANN, RBFNN, and SVM models to
anticipate their highest accuracy for different parameter tweaks. The
maximum accuracy obtained was 96.49% while utilising 20% test
data and 30 characteristics with k=5. However, with k=5 and 20%
test data, the accuracy fell to 92.98% when employing a shorter
one-dimensional feature vector produced using ICA. Additionally,
adopting 10-CV for training and testing dropped the accuracy of
k-NN from 93.15% (30 features) to 91.04% (1 feature via ICA)
(**Figures 8 and 9**).

**Figure 8:** A graph showing the artificial neural network’s accuracy. **Note:** 20% Test data with 30 features; 10-fold CV with 30 features; 20% test data with 1 feature; 10- fold CV with 1 feature.

**Figure 9:** A visualization of the performance of the neural networks with radial basis functions using an accuracy graph. **Note:** 20% Test data with 30 features; 10-fold CV with 30 features; 20% test data with 1 feature; 10- fold CV with 1 feature.

For ANN, utilising the top 30 characteristics and 20% test data generated 99.12% accuracy with four neurons. The impact of ICA in compressing to one feature enhanced accuracy to 91.23% for nine neurons. However, the accuracy fell from 97.54% to 90.51% after applying 10-CV. RBFNN’s spread value was determined between 0 and 60 with the greatest accuracy employing 20% test data and 10/5-CV, obtaining 95.12% accuracy at a spread value of 48. With a smaller one-dimensional feature vector utilising ICA, the accuracy reduced to 90.35%, but it rose to 90.49% with 10-CV.

SVM performance was assessed using different kernel function values. For 20% test data, SVM applying a linear kernel had an accuracy of 98.25% with 30 features and 90.35% with 1 lowered feature by ICA. ICA boosted the accuracy of SVM with RBF kernel from 89.47% to 91.23% (1 feature). However, when 10-CV was applied, the accuracy fell from 97.54% (30 features, linear kernel) to 90.33% and 90.86%, respectively, for RBF and polynomial kernels (1 feature).

The classifiers’ performance metrics, such as accuracy, specificity,
sensitivity, F-score, Youden’s index, and discriminant power, were
compared for each parameter variation. ROC curves of three categories
were also presented for visual comparison. The measurement
utilises 10-CV and an ICA-compressed one-dimensional feature
vector. The confusion matrix in **Table 3 **demonstrates the classifier’s
performance, displaying much improved true values as compared
to classification with one feature missing using ICA.

k-NN classifier (k-6) | ANN classifier (neuron number 7) | ||||||||
---|---|---|---|---|---|---|---|---|---|

Predicted Measurement | Predicted Measurement | ||||||||

True Measurement | Cancerous | Harmless | True Measurement | Cancerous | Harmless | ||||

IF | 30F | IF | 30F | IF | 30F | IF | 30F | ||

Cancerous | 338 (TP) | 346 | 19(FN) | 11 | Cancerous | 346 (TP) | 357 | 11(FN) | 0 |

Harmless | 32(FP) | 28 | 180(TN) | 184 | Harmless | 43(FP) | 14 | 169(TN) | 198 |

RBFNN classifier (spread =28) | SVM classifier (σ =1.3) | ||||||||

Predicted Measurement | Predicted Measurement | ||||||||

True Measurement | Cancerous | Harmless | True Measurement | Cancerous | Harmless | ||||

IF | 30F | IF | 30F | IF | 30F | IF | 30F | ||

Cancerous | 345 (TP) | 334 | 12(FN) | 23 | Cancerous | 348 (TP) | 343 | 14(FN) | 9 |

Harmless | 43(FP) | 138 | 169(TN) | 74 | Harmless | 43(FP) | 13 | 169(TN) | 199 |

**Tab. 3.** Comparing the confusion matrices of the classifiers with one reduced feature (1F) with the original 30 features allows for an investigation of the performance of classifiers with decreased dimensionality using Independent Component investigation (ICA).

k-NN | ANN | RBFNN | SVM (RBFK) | |||||
---|---|---|---|---|---|---|---|---|

IF | 30F | IF | 30F | IF | 30F | IF | 30F | |

F-score | 92.98 | 94.65 | 92.76 | 98.07 | 92.61 | 80.57 | 93.04 | 96.21 |

DP | 2.539 | 2.912 | 2.655 | InF | 2.606 | 1.131 | 2.769 | 3.267 |

Y | 0.795 | 0.839 | 0.766 | 0.934 | 0.763 | 0.284 | 0.772 | 0.899 |

Accuracy | 91.03 | 93.14 | 90.5 | 97.53 | 90.49 | 87.17 | 90.86 | 95.25 |

Specificity | 84.9 | 87.26 | 79.71 | 93.39 | 79.71 | 34.9 | 79.71 | 93.86 |

Sensitivity | 94.67 | 96.63 | 96.91 | 100 | 96.63 | 93.55 | 97.47 | 96.07 |

**Tab.4. **An analysis of the impact of the ICA algorithm on various performance measures (including sensitivity, specificity, accuracy, and F-score as a percentage) for the classifiers.

Criterion | k-NN | ANN | RBFNN | SVM | ||||
---|---|---|---|---|---|---|---|---|

IF | 30F | IF | 30F | IF | 30F | IF | 30F | |

AUC | 0.88 | 0.911 | 0.879 | 0.956 | 0.881 | 0.877 | 0.879 | 0.945 |

95%CI | 0.86-0.92 | 0.89-0.94 | 0.85-0.91 | 0.94-0.98 | 0.85-0.91 | 0.85-0.91 | 0.85-0.91 | 0.92-0.97 |

**Tab.5. **Criterion values of the ROC curves of ����-NN, ANN, RBFNN, and SVM

In summary, the study comprehensively investigated the performance
of k-NN, ANN, RBFNN, and SVM classifiers with varied
parameter values and underlined the effect of feature reduction using
ICA on their accuracy and discriminative powers. The results
give significant insights into the efficacy of these algorithms for
breast cancer categorization tasks (**Figure 10**).

**Figure 10 :** A series of graphs demonstrating the effectiveness of several Kernel Machine classifiers.

**Table 4** examines the impact of ICA on the k-NN, ANN, RBFNN,
and SVM models using many performance metrics such as sensitivity,
specificity, accuracy, F-score, Discriminant Power (DP), and
Youden’s index.

Discriminant power examines a classifier’s ability to differentiate between positive and negative data. ANN and SVM demonstrate remarkable separation abilities, with DP ranging from 3 to 30 for 30 different characteristics. When reducing to one dimension, SVM and ICA (ANN) generate DP values of 2.769 and 2.655, respectively, suggesting efficient discriminators.

Youden’s index, which represents a classifier’s capacity to avoid false predictions, attains its best value when k-NN is applied. The classi- fier’s ROC curve, based on Youden’s index, depicts the true positive rate (sensitivity) as a function of the false positive rate (1-specificity). The ROC curve is used to measure the area under the curve (AUC) and its 95% confidence interval (CI). An AUC of 1 reveals flawless classifications, whereas AUC larger than one implies more precise conclusions. The 95% CI, another ROC curve metric, analyses the classifier’s ability to discriminate between classes; quantities bigger than zero signal effective discrimination.

**Figure 11 **depicts the ROC curves for k-NN, ANN, RBFNN, and
SVM models using a one-dimensional feature vector lowered by
ICA and the original 30 features. The results reveal insights into the
models’ performance and illustrate the advantages of applying ICA
in classification tasks.

**Figure 11 :** To evaluate the sensitivity and specificity of the kNN, ANN, RBFNN, and SVM classifiers, we generated Receiver Operating Characteristic (ROC) curves. SVM RBF (30 features).

**Table 5** offers the classifier ROC curves required. AUCs of ANN
and SVM improve (0.966 and 0.949) with 30 additional features.
For SVM (0.885) and k-NN (0.897), AUC is increased when using
a single feature reduction by ICA. This implies SVM and k-NN successfully segregate data using a single feature.

**Table 5 **reveals that NN trumps ANN, RBFNN, and SVM with
a superior k-accuracy of 91.03% compared to their accuracies of
90.50%, 90.49%, and 90.86%, respectively, when using ICA.
Interestingly, k-NN, ANN, and SVM indicate decreased accuracy
when one feature is omitted, yet RBFNN’s performance rises. **Table 6** illustrates a comparison of the computation times for each
categorization strategy with the original 30 criteria.

Discriminator | Segmentation | Intercommunication duration (seconds) | 30 attributes(seconds) |
---|---|---|---|

k-NN | 20% 10-CV | 8.02 13.52 | 8.31 14.77 |

ANN | 20% 10-CV | 11.12 76.72 | 13.9 118.21 |

RBFNN | 20% 10-CV | 14.9 90.49 | 20.3 129.84 |

SVM (poly) | 20% 10-CV | 7.17 7.47 | 7.28 9.13 |

SVM(RBFK) | 20% 10-CV | 9.02 10.72 | 43.30 19.05 |

**Tab.6.** The execution time required for the classification process.

Author | Method | Feature number | Accuracy | Sensitivity |
---|---|---|---|---|

Krishnan, et al.[33] | 40% test data, SVM (poly) 40% test data, SVM (RBF) | 30 | 92.62% 93.72% | 92.69% 94.50% |

Bagui,et al.[34] | 64% test data, k-RNN 64% test data,k-RNN | 30 Best 3 | 96.00% 98.10% | 95.09% 98.05% |

Sweilam,et al.[35] | PSO+ SVM QPSO+SVM | 30 | 93.52% 93.06% | 91.52% 90.00% |

Mangasarian,et al.[36] | 10-CV,MSM-T | Best 3 | 97.50% | |

Mert,et al.[37] | 10-CV, PNN LOO,PNN |
3 (2IC+DWT) | 96.31% 97.01% | 98.88% 97.78% |

Zhang,et al.[38] | k-SVM | 6 | 97.38% | |

This study | 10-CV,k-NN | 1 feature reduced by ICA | 91.03% | 94.67% |

40 % test, k-NN | 92.56% | 94.02% | ||

10-CV,ANN | 90.50% | 96.91% | ||

40% test, ANN | 90.89% | 97.00% | ||

10-CV,RBFNN | 90.49% | 96.63% | ||

40% test, RBFNN | 89.98% | 96.01% | ||

10-CV,SVM (linear) | 90.33% | 96.35% | ||

40% test, SVM (linear) | 90.01% | 95.00% | ||

10-CV,SVM (quadratic) | 89.98% | 95.24% | ||

40% test, SVM (quadratic) | 91.01% | 96.42% | ||

10-CV,SVM (RBF) | 90.86% | 97.47% | ||

40% test, SVM (RBF) | 91.03% | 97.56% |

**Tab.7.** An analysis of the accuracy and methods used in previous research compared to the results and techniques of this study.

In terms of processing time, the proposed techniques outperform straightforward classification of the original dataset. Creating an Independent Component (IC) for classification is significantly less complex than designing a neural network with 30 variables. When ANN and RBFN were divided by 20%, the processing durations dropped from 13.9 to 11.12 seconds and from 20.03 to 14.9 seconds, respectively. The introduction of IC as a feature leads in a noticeable drop in complexity during 10-fold cross-validation, lowering ANN and RBFNN usage durations from 118.21 to 76.72 seconds and 129.84 to 90.49 seconds, respectively. ICA also increases SVM and k-NN estimates substantially.

Breast cancer is more frequent in women, underscoring the signifi- cance of early detection to minimise death rates. Machine learning algorithms, such as SVM, excel in spotting breast cancer occurrences in data. This research makes use of the scikit-learn, pandas, seaborn, matplotlib, and numpy tools, and the breast cancer dataset was imported using scikit-learn’s load breast cancer function. Exploration of the dataset’s features, objective variables, and relationships indicates significant drivers for effective breast cancer diagnosis while avoiding over fitting by deleting marginally relevant properties.

Other visualisations, such as heat maps, pair plots, count plots, and
scatter plots, offered insights into the data’s properties. The identi-
fication of numerous significantly connected features showed their
potential as effective breast cancer indicators. These findings stress
the necessity of feature selection in creating an efficient and accurate
classification model **Figures 12-15**.

**Figure 12:** Pair plot of cancer data with selected features.

**Figure 13:** Count plot distribution of target in cancer data.

**Figure 14:** Scatterplot of mean area vs. mean smoothness with target.

**Figure 15:** Correlation heat map of cancer data features.

To divide the dataset into training and testing sets, we utilised the
scikit-learn test-split technique. SVM training on the training set
gave 96% accuracy, exhibiting excellent performance. **Figures 16** and 17 show the heatmap and categorization report, respectively.

**Figure 16:** Heat map of 96% accuracy.

**Figure 17:** Classification report of 96% accuracy.

To increase the model’s performance, we incorporated normalisation
and scaling features with a mean of zero and a standard deviation
of one. Through feature aggregation, our strategy lowered
the effect of outliers while boosting accuracy. We then fine-tuned
SVM’s hyper parameters using scikit-Grid Search, adjusting C and
gamma values. This adjustment resulted in a 97% increase in accuracy.
The graphic in **Figure 18** and the categorization report in **Figure 19** clearly illustrate the large increase gained with these improvements

**Figure 18:** Heat map of 97% accuracy.

**Figure 19:** Classification report of 97% accuracy.

We were able to properly identify breast cancer using the SVM technique. Machine learning technologies have the potential to enhance recognition models. More study is necessary to find techniques and strategies for enhanced efficiency.

#### Discussion

Sensitivity and specificity are crucial parameters in cancer classification,
reflecting the accurate identification of genuine positives and
negatives, respectively. Interestingly, when applying a one-dimensional
feature vector reduced by ICA, the accuracy of classifiers substantially
reduces, but SVM and RBFNN classifiers demonstrate
increased sensitivity levels. Particularly, SVM with an RBF kernel
and just one feature delivers the maximum sensitivity level. **Figure
20 **simply demonstrates the impact of ICA on the sensitivity parameters
of the classifier.

**Figure 20:** A comparison of the sensitivity scores among the classifiers.

Greater sensitivity in cancer categorization indicates enhanced detection
of malignant samples, enabling doctors to more properly
identify carcinogenic masses and distinguish malignant tumors. To
analyse the impact of ICA-based feature reduction, **Table 7 **displays
accuracy and sensitivity metrics from past classification efforts,
alongside the newest study utilising the WDBC dataset. Notably,
the WBC dataset was also utilised in research unrelated to the
WDBC study.

The incorporation of a large number of criteria assists the differentiating between benign and aggressive breast cancer patients. The reduction of ICA features into a single feature has a major effect on the accuracy of k-NN, ANN, and SVM classifiers. This feature reduction, on the other hand, boosts the sensitivity and accuracy of SVM and RBFNN classifiers.

As indicated in **Table 7**, the analysis of classifiers applying the
one-dimensional feature vector created by ICA offers remarkable
results, outperforming other approaches in terms of sensitivity
measures. Nonetheless, the recommended categories’ accuracy ratings
are substantially lower (90.53% 0.34%) than those derived by
prior approaches (94.93% 2.07). For example, employing a 6-dimensional
feature space obtained by the K-means approach, a hybrid
methodology combining Discrete Wavelet Transform (DWT)
and ICA gave exceptional 10-CV accuracy rates of 96.31% and
97.38% with SVM and PNN, respectively. Notably, WDBC data
providers attained the greatest accuracy of 97.50% by applying the
Multisurface Method Tree (MSMT) with three provided features
[38]. In terms of scores, additional SVM-based study applying 30
criteria [36,38] indicated equivalent outcomes to our one-dimensional
findings.

#### Conclusion

In this research, we aim to explore the impact of dimensionality reduction using Independent Component Analysis (ICA) on breast cancer decision support systems, employing a diverse range of classifiers such as Artificial Neural Networks (ANN), k-Nearest Neighbors (k-NN), Radial Basis Function Neural Networks (RBFNN), and Support Vector Machines (SVM). We will compare the results obtained from the reduced one-dimensional feature vector created using ICA with the original thirty features of the Wisconsin Diagnostic Breast Cancer (WDBC) dataset.

The early findings show remarkable trends in the categorization accuracy rates. While the accuracy rates for other classifiers declined considerably by using the less features, the RBFNN classifier stood out dramatically. It demonstrates a large boost in accuracy from 87.17% to 90.49% using the one-dimensional feature vector. Moreover, both RBFNN and SVM demonstrated improved sensitivity rates for accurately categorising malignant samples, suggesting its potential in identifying breast cancer patients effectively.

The findings shows that feature reduction using ICA may be a desirable method, particularly when endeavouring to enhance the detection rate of malignant breast cancer patients consistently, while preserving good accuracy. Additionally, this strategy may lead to lower computational complexity, making it an appealing choice for practical applications. As we delve further into the inquiry, we aim to unearth novel insights about the benefits and limitations of ICA- based dimensionality reduction in breast cancer diagnosis.

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