Abstract
Chronic diseases are the leading cause of death and disability worldwide, necessitating early detection and management to mitigate their adverse effects on health and improve quality of life. Leveraging machine learning algorithms has become a prominent approach in predicting the risk of various chronic diseases. These algorithms excel in analyzing complex datasets to identify patterns and risk factors associated with chronic conditions. This study explores the application of two different machine learning algorithms on an open-source dataset to predict the risk of chronic diseases. The outcomes of these implementations are analyzed and discussed in the final section, providing insights into their effectiveness and potential for enhancing chronic disease management.
| Original language | English |
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| Title of host publication | 2024 IEEE 3rd World Conference on Applied Intelligence and Computing, AIC 2024 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1007-1012 |
| Number of pages | 6 |
| ISBN (Electronic) | 9798350384598 |
| DOIs | |
| Publication status | Published - 2024 |
| Event | 3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024 - Hybrid, Gwalior, India Duration: 27 Jun 2024 → 28 Jun 2024 |
Publication series
| Name | 2024 IEEE 3rd World Conference on Applied Intelligence and Computing, AIC 2024 |
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Conference
| Conference | 3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024 |
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| Country/Territory | India |
| City | Hybrid, Gwalior |
| Period | 27/06/24 → 28/06/24 |
Bibliographical note
Publisher Copyright:© 2024 IEEE.
Keywords
- Chronic Diseases
- Early Detection
- Health Data Analysis
- Machine Learning Algorithms
- Risk Prediction