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Review Papers | Health Sciences | United States of America | Volume 13 Issue 8, August 2024 | Rating: 5.4 / 10
Machine Learning Algorithms for Early Detection of Chronic Diseases in the Medicare Population
Ginoop Chennekkattu Markose [2]
Abstract: Diabetes, cardiovascular disease, and COPD are characteristic examples of chronic diseases that contribute greatly to the general healthcare cost, especially for the Medicare population. The best management of these conditions and the resulting morbidity and mortality is dependent on early screening for these conditions. Artificial Intelligence (AI) has become significant in the healthcare sector owing to the possibility of processing a large amount of data so as to compare with earlier data and infer the early stages of chronic diseases. This paper identifies and discusses several ML algorithms that can be used for the early detection of conditions in the Medicare population. As for the algorithms, the work is devoted to the aspects of data preprocessing, model selection, and evaluation measurements used in clinical practice. In this paper, we compare algorithms such as the SVM, RF, and NN using a Medicare dataset. Hence, the study reveals that embracing the use of modern technologies such as ML could help Medicare easily detect chronic diseases, leading to more effective treatment and or management of such patients, as well as rational use of available resources. Lastly, the paper includes points of concern, possibilities of having bias, and directions for future work when using ML in this area.
Keywords: Chronic diseases, Machine Learning, Medicare, Early detection, Support Vector Machines, Random Forest, Neural Networks, Healthcare
Edition: Volume 13 Issue 8, August 2024,
Pages: 1408 - 1416