Unsupervised Learning for Heart Disease Prediction: Clustering-Based Approach

This paper on the prediction of heart disease addresses the application of unsupervised machine learning algorithms, digs up the latent pattern of risk in the data of patients for early diagnosis, and intervenes. We have compared models K-Means Clustering, DBSCAN, Agglomerative Clustering, Gaussian Mixture Model, and Spectral Clustering, wherein K-Means brought out the best result that happened to be 84 percent with the groups formed for patients using nuanced risk indicators. For such insights, the project embeds an HTML web-based interface where healthcare professionals and patients alike can easily read predictions. This approach advances predictive accuracy, yet brings to the medical profession an incredibly powerful tool for a more personalized type of care. Providers would then have the ability to identify ahead of time high-risk people and monitor their care more carefully. It, however, opens up the possibility of unsupervised learning in health analytics and shows how this can be applied to the role of machine learning for early detection and targeted treatment, thereby contributing to better patient outcomes and proactivity in managing heart disease risks.