Heart Disease Prediction System Using Machine Learning

Abstract

The major killer cause of human death is Heart Disease (HD). Many people die due to this disease. Lots of researchers have been discovering new technologies to prognosticate the disease early before it ’ s too late for helping healthcare as well as people. These processes are still under research phase. Machine Learning (ML) is faster-emerging technology of Artificial Intelligence (AI) that contributes various algorithms for HD. Based on the proposed problem, ML provides different classification algorithms to divine the probability of patient having HD. For predicting HD, a lot of research scholars contributes their effort in this work using various techniques and algorithms such as Decision Tree (DT), Naïve Bayes (NB), Support Vector Machine (SVM), KNN (K-Nearest Neighbor), Neural Network (NN), etc. In order to give some effort on this work, we are going to develop a Web-based Heart Disease Prediction System (HDPS) by applying DT and NB ML algorithms. We are using the UCI repository HD dataset to train a model by comparing DT and NB algorithm for HDPS Web application. The dataset contains 303 instances with 14 attributes that help to train a prediction model that will be deployed into a web application for prediction. The main aim of this project is to build an efficient prediction model and deploy for prediction of disease. An HDP Model is built by using NB algorithm that provides 88.163% accuracy among others. A web-based HDPS application is developed through the waterfall model. Each phase is efficiently done. The project is successfully created with help of requirement analysis and project plan, system design, database design, testing plan, identifying features and functionalities, and system validation and deployment. The limitation of this project is to have only predicted the presence of heart disease but not identify which type of HD does have at patient. In future work, we can enhance the project by appending more detail prediction of HD at patient and incorporate with smart wear devices that integrate to Hospital Emergency System.