Predicting Heart Disease: a Comprehensive Approach to Data Analysis and Machine Learning Model Development

This paper presents a detailed overview of an end-to-end machine learning (ML) model development process, with the goal of establishing a precise and fast artificial intelligence (AI) system for heart disease prediction on real-world data. The study also showcases how good practices in data analysis and proper data processing can help in providing reliable and accurate results, even when working with a small number of initial data points. Predictive models are trained on publicly available data, and a subset of common medical indicators is used to detect any increased likelihood of persons developing a heart disease. Our findings outline the essential steps required for a thorough ML model training study, which are critical for optimizing the performance of the algorithms. The results underline the capability of ML techniques to predict the risk of heart disease with considerable accuracy, thereby highlighting the transformative potential of AI in the area of bioinformatics and intelligent systems for clinical diagnostics.