Heart Disease: Automatic Prediction from the Numerical and Categorical Features by Machine Learning Methods

A massive chunk of data is generated by the medical industry, which requires it to be processed using the latest algorithms, which will help us to de-duce effective and accurate findings, make proper decisions based on the analysis, and obtain the desired results. Heart disease is the main issue and one of the main contributors to the number of fatalities worldwide. This study makes use of various machine learning algorithms, including Logistic Regression, Support Vector Classifier, Random Forest, Decision Tree Classifier, Gaussian Naive Bayes, K-Nearest Neighbor, and Stochastic Gradient Descent, for various sets of best features and training-testing data distribution, to develop an efficient model for heart disease prediction. 13 characteristics, including age, gender, blood pressure, cholesterol, obesity, and cp, are used in the model. We are using a user-friendly system that comprises several phases. The Heart Disease Prediction Model, proposed with this study’s help, gives accurate results and is a perfect approach that can be used in the heart disease prediction fields.