Improving the Accuracy of Heart Disease Prediction Approach of Machine Learning Algorithms

Abstract

The work is about forecasting heart disease. First and foremost, we gathered data from various sources and divided it into two portions, one of which is 80% and the other is 20%, where the first part is for training and the remainder is reserved for the test dataset. After collecting this dataset, we applied the pre-processing formula and different classifier algorithms. K-Nearest Neighbor, Support Vector Machine, Decision Tree, Random Forest, Naive Bayes & Logistic Regression are the techniques utilized here. When compared to other algorithms, Logistic Regression, KNN, and SVM provided the same or superior accuracy. Precision, Recall, F1 score, and ERR are used to measure accuracy. Gender, Glycogen, BP, and Heartrate are some of the prefixes used while training and found to be different major vulnerable factors of heart diseases. The direction of this work is real-life experiments and clinical trials using different devices.