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Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
Published on: September 26, 2018
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Automated Cardioailment Identification and Prevention by Hybrid Machine Learning Models
K S Archana1, B Sivakumar2, Ramya Kuppusamy3
1Department of Computer Science and Engineering, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, India.
Computational and Mathematical Methods in Medicine
|February 25, 2022
Summary
Machine learning models accurately predict cardiovascular disease (CHD) risk using patient data. This approach improves early detection and supports healthcare professionals in competent patient analysis.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Healthcare
Background:
- Cardiovascular disease (CHD) is a leading global cause of death, necessitating accurate early prediction for effective treatment.
- Traditional prediction methods struggle with complex data and relationships, highlighting the need for advanced analytical techniques.
Purpose of the Study:
- To apply machine learning (ML) to predict heart disease risk from historical medical data.
- To uncover data correlations for improved prediction accuracy using various ML models.
Main Methods:
- Implementation of Naive Bayes and Random Forest algorithms, including hybrid approaches.
- Utilizing 14 key patient parameters such as age, sex, blood sugar, and chest discomfort for analysis.
Main Results:
- Achieved a high prediction accuracy of 93% for identifying heart disease.
- The system provides probability percentages for developing heart disease.
Conclusions:
- The proposed ML system effectively identifies heart disease, offering improved prediction accuracy.
- This method aids physicians in competently analyzing heart patients and potentially reducing mortality rates.
