HEART DISEASE PREDICTION USING MACHINE LEARNING
S. M. Sasikala, AP/CSE Computer Science and Engineering & Dhirajlal Gandhi College of Technology
Mr.K.V.Sridhar Computer Science and Engineering & Dhirajlal Gandhi College of Technology
Mr.S.M.Sriram Computer Science and Engineering & Dhirajlal Gandhi College of Technology
Mr.D.Tamilarasan Computer Science and Engineering & Dhirajlal Gandhi College of Technology
Mr.J.S.Vittal Computer Science and Engineering & Dhirajlal Gandhi College of Technology
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Abstract - Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. The traditional approach to predicting heart disease involves a comprehensive evaluation of multiple risk factors such as age, gender, smoking status, blood pressure, cholesterol levels, and family history. However, recent advances in machine learning have enabled healthcare professionals to develop more accurate and efficient models for heart disease prediction using large datasets and advanced algorithms. This prediction demonstrated several classification mechanisms to build the prediction model. The data was collected and cleaned from any missing values and extreme outliers. The results show that SVM and random forest models are highly accurate and effective in identifying patients who are at risk of heart disease. Further many unprocessed machine learning algorithm techniques will be processed with the dataset by firstly collecting the dataset and cleaning it by training and testing the data, and the missing values are removed. Once the model is trained and validated, deploy it in a web application that can be used by doctors or patients to predict the risk of heart disease based on their medical information.
Key Words: Heart disease prediction, Machine learning, Support vector machine, Multilayer perceptron, Naïve bayes, Random forest.