The Role of Machine Learning in Early Detection of Chronic Diseases through Wearable Data
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V6I2P119Keywords:
Machine Learning, Chronic Diseases, Real-Time Data, Supervised Learning, Unsupervised Learning, Early Detection, Patterns, Wearable Devices, Data Analytics, K-Means Learning, Logistic RegressionAbstract
In this study, an insight into the various methods utilised by medical practice to facilitate patient diagnostics of early detection of chronic diseases using concepts of machine learning. One of the major implications that has been presented in the study includes a variety of models that enable the application of machine learning in different areas of disease detection, concerning the factors of accuracy and precision. The complexities that lie within the utilisation of such models have been evaluated based on secondary data and an extensive literature review such that its feasibility in healthcare context can be studied effectively. The methods applied to study the role of machine learning include case studies and scholarly articles that provide better insights into research that has been carried out in the medical field concerning the effectiveness of detecting and monitoring health attributes of individuals. The contributions made through the study have been discussed with an implication towards future research that can be carried out as a part of its application in a practical essence
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