Exploring Heart Disease Prediction
Imagine a journey into the world of heart disease prediction, where data science and healthcare analytics converge to unravel the mysteries of this critical health issue. This project was a comprehensive exploration, divided into two interconnected parts, each adding depth and insight to our understanding of heart disease prediction.
Part 1
In the initial phase of our project, we set the stage by delving into the fundamental concepts of machine learning and data mining as applied in healthcare. We conducted a thorough literature review, explored our dataset through data visualization and feature engineering, and built decision tree models using R. Our focus was on understanding the core aspects of heart disease prediction. The complete story of this phase can be found in the first article on Medium. Dive into it here: Link to Part One Article
Part 2
The second part of our project took us even deeper into the heart of predictive modeling. Building upon the foundation laid in the first part, we ventured into trying different classification techniques, including neural networks, support vector machines, and naive Bayes. We conducted rigorous model evaluations and comparisons, all with the aim of gaining critical insights into heart disease and its key predictive factors. For the complete narrative of this phase, don’t miss the second article on Medium. Explore it here: Link to Part Two Article