Heart Disease Predictive Analysis Using Machine Learning Approaches

Machine Learning (ML) has found widespread applications in the healthcare sector worldwide, including the diagnosis and treatment of heart diseases, locomotor disorders, and various other medical conditions. ML methods have revolutionized healthcare by enabling the analysis of large and complex medical datasets, leading to valuable insights and predictions that aid healthcare professionals in providing better patient care. ML's ability to analyze vast amounts of healthcare data, uncover patterns, and make predictions has significant potential to improve patient outcomes, optimize medical workflows, and advance medical research. However, it's essential to address privacy and ethical considerations when using ML in healthcare, ensuring the responsible and secure use of sensitive patient information. Supervised Learning methods like SVM, Random Forest, and Logistic Regression are used for the analysis of the dataset downloaded from Kaggle. The various performance parameters such as Precision, F-1 score, Accuracy, and Recall were used to compare the performance of different ML classification techniques. Among the various methods evaluated, the Random Forest classification algorithm was found to outperform the other methods across the fourteen available parameters.