Machine Learning Models for Heart Attack Prediction
Published:
This project is about the general analysis of machine learning models for heart attack prediction by using various analytical techniques to gain awareness of the structure and characteristics of the dataset. The research begins with an Exploratory Data Analysis (EDA) and extending into the distribution of individual features and the relationships among them. Correlation analysis is then employed to found potential interactions and dependencies among numerical variables, shedding light on their collective impact on heart disease risk. Moving beyond correlation, cluster analysis is applied to identify underlying patterns or subgroups within the data, indicative of specific risk groups or heart disease profiles. The final stage involves the development of predictive models, utilizing the dataset’s wealth of information to predict heart disease diagnosis accurately. The final goal is to contribute to early detection and intervention strategies. This multifaceted approach, encompassing EDA, correlation analysis, cluster analysis, and predictive modelling, aims to enhance our understanding of heart disease prediction.