Detecting Cardiovascular Diseases Using Machine Learning and Biomedical Data Classification

Since cardiovascular disease (CVD) is still one of the world's leading causes of death, prompt intervention depends on early identification. Conventional diagnostic techniques depend on the manual interpretation of biological data, which can be laborious and error-prone. Using biological data, this study investigates the use of techniques based on machine learning (ML) for CVD detection and classification. Accuracy, recall, accuracy, and F1-score are used to compare and evaluate different categorization models, such as Support Vector Machines (SVM), Random Forests (RF), & artificial neural networks (ANNs). Important biomedical variables like blood pressure, cholesterol, beat variability, and ECG values are included in the collection. Techniques for feature selection are used to maximize the prediction power of the model. The experimental findings show that ML-based classification greatly improves CVD detection accuracy. Important biomedical variables like blood pressure, cholesterol, heart rate variation, and ECG values are included in the collection. Techniques for feature selection are used to maximize the prediction power of the model. When compared to conventional techniques, the experimental results show that ML- based classification greatly improves the efficacy of CVD detection. The results highlight how ML might help medical practitioners diagnose diseases early, which can eventually improve patient outcomes.