Enhance the Performance of Cancer and Diabetics Prediction by Techniques of Machine Learning

  Samrudhi Jayashri Rajendra Kaware,  Vinod S Wadne

these systems allow the user to make a use of algorithms to predict the risk of diabetes mellitus in human body. The various classification models such as Decision Tree, artificial Neural Networks, Logistic Regression, Association rules and Naive Bayes are used in this system. Then the Random Forest technique is used to find the accuracy of each model in the system. The dataset used is the Pima Indians Diabetes Data Set, which has the information of patients, some of them have developing diabetes therefore, this project is aimed to create a mobile application for predicting a person’s class whether present in of the diabetes and cancer risk or not. In this study, we have analyzed medical data using several classification algorithms in order to optimize classifier performance for cancer and diabetes prediction. For this project we have guidance letter from “Max Care Hospital” under support of medical oncologist Dr. Satish Sonawane (onco-surgeon ) to study the medical history of patients and their medical reports.

Keywords- Decision tree, ANN, Health-Care,ELM

Unique Identification Number - IJEDR2002014
Page Number(s) - 64-68
Pubished in - Volume 8 | Issue 2 | June 2020
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)

  Samrudhi Jayashri Rajendra Kaware,  Vinod S Wadne,   "Enhance the Performance of Cancer and Diabetics Prediction by Techniques of Machine Learning", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.8, Issue 2, pp.64-68, June 2020, Available at :http://www.ijedr.org/papers/IJEDR2002014.pdf