Cardiac disease risk prediction using machine learning algorithms
Albert Alexander Stonier1, Rakesh Krishna Gorantla2, K Manoj2
1Department of Energy and Power Electronics, School of Electrical Engineering Vellore Institute of Technology Vellore India.
Insights
This study developed a machine learning (ML) system to predict heart attack risk. The Random Forest algorithm achieved 88.52% accuracy, offering a promising tool for early cardiovascular disease detection.
Area of Science:
- Cardiology
- Medical Informatics
- Machine Learning
Background:
- Heart attacks, often caused by coronary disease, are a leading cause of death.
- Early detection and management of heart disease risk are crucial for prevention and reducing healthcare costs.
- Machine learning (ML) is increasingly vital for predicting disease occurrence in healthcare.
Purpose of the Study:
- To develop a predictive system for heart attack risk assessment.
- To analyze diverse data sources, including electronic health records and clinical reports.
- To leverage ML for improved diagnostic capabilities in cardiovascular health.
Main Methods:
- Application of various machine learning algorithms for predictive analysis.
- Comparison of Random Forest, Regression models, K-nearest neighbour imputation (KNN), and Naïve Bayes algorithms.
- Utilizing patient data from electronic health records and clinical diagnosis reports.
Main Results:
- The Random Forest algorithm demonstrated superior performance in forecasting heart attack risk.
- An accuracy of 88.52% was achieved by the Random Forest model.
- Comparative analysis highlighted the effectiveness of Random Forest over other tested ML methods.
Conclusions:
- Machine learning, particularly the Random Forest algorithm, shows significant potential for accurate heart attack risk prediction.
- This approach could revolutionize the diagnosis and treatment of cardiovascular illnesses.
- Early prediction systems can enhance patient outcomes and optimize medical resource allocation.
Abstract:
Heart attack is a life-threatening condition which is mostly caused due to coronary disease resulting in death in human beings. Detecting the risk of heart diseases is one of the most important problems in medical science that can be prevented and treated with early detection and appropriate medical management; it can also help to predict a large number of medical needs and reduce expenses for treatment. Predicting the occurrence of heart diseases by machine learning (ML) algorithms has become significant work in healthcare industry. This study aims to create a such system that is used for predicting whether a patient is likely to develop heart attacks, by analysing various data sources including electronic health records and clinical diagnosis reports from hospital clinics. ML is used as a process in which computers learn from data in order to make predictions about new datasets. The algorithms created for predictive data analysis are often used for commercial purposes. This paper presents an overview to forecast the likelihood of a heart attack for which many ML methodologies and techniques are applied. In order to improve medical diagnosis, the paper compares various algorithms such as Random Forest, Regression models, K-nearest neighbour imputation (KNN), Naïve Bayes algorithm etc. It is found that the Random Forest algorithm provides a better accuracy of 88.52% in forecasting heart attack risk, which could herald a revolution in the diagnosis and treatment of cardiovascular illnesses.
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