Early stage breast cancer detection using ensemble approach of random forest classifier algorithm


Author(s): Dumpala Shanthi

Breast Cancer is one of the most deadly diseases in the world and is commonly seen in women. Based on the severity, breast cancer is classified into two types. One is Benign type of breast cancer, which can be detected at early stages and can be cured with the help of medication. Other is Malignant type of breast cancer, which shows severe affect and might lead to death. To detect breast cancer at early stages, wide variety of algorithm techniques are used such as Navie Bayes, Convolution Neural Network, KNN, adaptive voting ensemble machine learning algorithm and so on. Most latest algorithm that is under practice is adaptive voting ensemble machine learning algorithm. In this algorithm, Wisconsin Breast Cancer dataset and CNN algorithm is used to classify images and for object detection. But the major drawback of ensemble machine learning algorithm is lack of accuracy. It is proved that Neutral Network works more effective on humans mostly in analysing data and to perform pre-diagnosis without medical knowledge. In this paper, we propose Random Forest Classifier algorithm to achieve more accuracy.

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Awards Nomination

Editors List

  • Prof. Elhadi Miskeen

    Obstetrics and Gynaecology Faculty of Medicine, University of Bisha, Saudi Arabia

  • Ahmed Hussien Alshewered

    University of Basrah College of Medicine, Iraq

  • Sudhakar Tummala

    Department of Electronics and Communication Engineering SRM University – AP, Andhra Pradesh




  • Alphonse Laya

    Supervisor of Biochemistry Lab and PhD. students of Faculty of Science, Department of Chemistry and Department of Chemis


  • Fava Maria Giovanna


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