HDP: Simulated IoT Architecture Design Applying GridSearchCV

Globally, cardiovascular disease remains a major burden of morbidity and death with more than half a billion affected population. Heart Disease Prediction (HDP) is strenuous for medical staff because of the risk factors. Classifying people into various risk groups according to their traits and medical history can be done effectively and powerfully leveraging machine learning techniques. This research study leverages the Framingham Heart Study dataset to propose a machine learning enabled system regarding heart disease prediction (HDP). Utilizing GridSearchCV, six supervised machine learning algorithms were trained and optimized: gaussian naive bayes, k nearest neighbour, decision tree, support vector machine, random forests, and logistic regression. With 97.9% accuracy, 96.4% precision, 99.5% recall, and 97.9% f1-score, random forest outperformed the six algorithms with a 10-fold validation technique. To illustrate the prediction model’s possible real-time integration into patient monitoring systems, a notional Internet of Things (IoT) architecture was created. Even though the architecture is now just in simulation, it offers a framework for future use with real IoT infrastructure. The results demonstrate that combining ML with IoT for early cardiovascular risk prediction and decision assistance is both feasible and beneficial.