Personalized Cardiovascular Disease Risk Prediction Using Random Forest: An Optimized Approach

Abstract

This study investigates the advantages and disadvantages of using a variety of machine learning approaches to estimate an individual person's risk of Cardiovascular Disease (CVD). Not all relevant risk variables specific to an individual may be considered by conventional risk assessment methods because they rely on predetermined risk factors. Algorithms trained on big data sets can identify trends and anticipate individual risks, allowing for more precise and targeted measures against CVD. Different machine learning algorithms are tested for their ability to predict CVD risk using a dataset comprising clinical and demographic factors. Examples of these algorithms are logistic regression, decision trees, random forests, and Support Vector Machines (SVM). Additionally, we investigate how feature-selection strategies and model hyperparameter adjustment can improve ML model efficiency. The study reveals the most critical risk factors for CVD and underlines the potential of machine learning techniques to enhance personalized CVD risk prediction. This research took advantage of data from over 14,000 patients, including detailed information on their demographics, medical histories, and daily routines. The solution outperformed conventional approaches with an accuracy of 90% after training machine learning models on this varied dataset. The personalized data-driven strategy shows potential for improving CVD risk prediction and preventative measures.

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