Prediction of Heart Disease and Breast Cancer Using Random Forest (RF) and Multi-Layer Perceptron Neural Network (MLP) Approaches

Abstract

Disease diagnosis is one of most important application of data mining to proving successful results. Breast Cancer Diagnosis a re two medical applications which became a big challenge to the researchers. The use of machine learning and data mining techniques has changed the whole process of breast cancer Diagnosis. Most data mining methods which are commonly used in this domain are considered as classification category and applied prediction techniques assign patients to either a” benign” group that is non- cancerous or a” malignant” group that is cancerous he project focuses on the prediction of various diseases like heart disease and breast cancer that can assist medical professionals in predicting disease status based on the clinical data of patients. In existing method reveals that 91% accuracy by using random forest approach in breast cancer and heart disease datasets. In our proposed method reveal that 94.4% and 85% accuracy in random forest, and multi-layer perceptron approach reveals that 95.8% and 86.7% respectively in breast cancer and heart disease datasets .Its seems to be that our proposed methods are better than the existing method.