Sridhar Reddy Vulapula
Department of Information Technology, Vignana Bharathi Institute of Technology, Hyderabad, Telangana, IndiaPublications
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Review Article
Predicting heart disease using machine learning and IoT techniques
Author(s): N Divya*, Md Riyazuddin, Abdul Ahad, Sridhar Reddy Vulapula, A Manjula and Mohd Sirajuddin
Nowadays, there is an increase in heart disease in all age groups in society. It is therefore necessary to set up a machine learning system in order to be able to detect and prevent indications of heart disease at an early stage. There must also be a mechanism in place that is handy and at the same time capable of being trusted. Thus, we propose to create an application that can predict the potential for heart disease given basic symptoms such as age, gender, ECG, heart rate, chest pain, cholesterol, blood pressure, blood sugar. The method will use various models trained using machine learning algorithms such as the support vector machine, the Naïve Bayes classifier and the decision tree. The accuracy of the method will be measured and distinguished in order to select the best model for estimating heart disease. Latest advances in online healthcare can be used for IoT and sensing.. Read More»
Editors List
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Ahmed Hussien Alshewered
University of Basrah College of Medicine, Iraq
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Sudhakar Tummala
Department of Electronics and Communication Engineering SRM University – AP, Andhra Pradesh
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Alphonse Laya
Supervisor of Biochemistry Lab and PhD. students of Faculty of Science, Department of Chemistry and Department of Chemis
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Fava Maria Giovanna
- Manuprasad Avaronnan
Google Scholar citation report
Citations : 558
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