Author : Gangolla, Maneeshwar
European Journal of Molecular & Clinical Medicine,
2020, Volume 7, Issue 4, Pages 2453-2459
A Random Forest Machine Learning Algorithm is integrated with the Flask Web framework for predicting of Heart Disease is proposed. The ensemble learning methods are used for predicting heart disease. The proposed methodology involved integration of the Flask Web framework with the Random Forest machine learning technique to estimate the heart disease stages. Artery Blockage indicates the presence of heart disease. The higher the blockage, higher is the stage of heart disease. Stage 1 and Stage 2 indicate the presence of heart disease whereas Stage 3 and Stage 4 are called chronic heart disease and the risk of a heart attack at any day in such patients is very high. The Data required for the prediction contains parameters such as Age, Sex, Blood Pressure, Sugar levels which are collected from the Kaggle website. Experimental results say that predictions by using the proposed approach are consistently better than those obtained using the other methods.