Comparative Analysis of Machine Learning Algorithms for Heart Disease Predictions

• Published in: 2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS) pp. 1340 - 1344
• Main Authors: Patidar, Sanjay, Jain, Anvay, Gupta, Ayush
• Format: Conference Proceeding
• Language: English
• Published: IEEE 25-05-2022
• Subjects: Cardiac arrest; Cardiovascular Disease; Comparative Analysis; Control systems; Forestry; Heart; Heart Diseases; Machine learning algorithms; Machine Learning Regression Algorithms; Performance evaluation; Prediction algorithms
• DOI: 10.1109/ICICCS53718.2022.9788408
• EISSN: 2768-5330

The number one reason of deaths worldwide are cardiovascular diseases. An approximate of 17.9 million lives die because of CVDs every year, which means it is responsible for 31% of all deaths globally. Four out of five CVD deaths are because of heart assaults and strokes, and one-third of these deaths arise upfront in humans below 70 years of age. Heart failure is a not unusual occasion due to CVDs and this dataset consists of eleven functions that can be used to expect a probable heart sickness. This research work has used three different Machine Learning [ML] classifier models such as Logistic Regression Classifier, K-Nearest Neighbors Classifier, and Random Forest Classifier. These three Machine Learning models are then compared based on a five-evaluation metrics to find out the best suited model for disease detection.