Web-Based Machine Learning Algorithms for Personalized Cardiovascular Disease Risk Assessment
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Keywords
ML ,Web-Based, Accuracy, CSV, Random forest, Datasets
Abstract
Heart disease is the leading cause of death globally, claiming a life every minute. Early detection of heart disease can be challenging, but machine learning can accurately identify illnesses in the healthcare sector. This study used medical databases to analyze various heart disease situations. The data was analyzed using Python and the Random Forest algorithm. By predicting future patients based on past patient data, lives can be saved. The study developed a reliable method for predicting heart disease using the Random Forest algorithm. A technique was employed to utilize patient data from a CSV file and create a useful forecast of the likelihood of a heart attack. The prediction tool is web-based and enables users to input their information to determine their risk of developing heart disease. The method has numerous advantages, including high success rates, good performance and accuracy rates, as well as flexibility and adaptability.
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