Authors: Vikram S Tigadi, Yallaling R Dalawayi, Rajesh S Meti,, Rajguru M Hiremath, Professor Pooja C Shindhe
Abstract: Heart disease remains one of the leading causes of death throughout the world, and early detection is the key to improved patient outcomes. This paper introduces a Decision Support Heart Health Prediction System (DSHHPS) developed using machine learning techniques to help diagnose critical clinical and demographical data including age, BP level, cholesterol level, glucose level and other vital medical signs. The processed data is further sanitized using pre-cleaning, preprocessing and selection of features to make it reliable and accurate. Several different machine learning models are tested and compared The system evaluates many clinical information such as age, sex, blood pressure, cholesterol level, the results of the resting ECG reading, the type of chest pain and the amount of sugar in their bloodstream along with other important health readings. Rigor: The dataset is subjected to various cleaning, preprocessing and feature selection processes to remove inconsistencies and error prior to training the model. A number of machine learning models are experimented and compared to select the best one, which produces the most accurate predictions.
