Machine Learning Models for Diabetes Risk Assessment
Keywords:
Diabetes Prediction, Predictive Analytics, Healthcare, Machine Learning Algorithms, Clinical Data, Health Decision MakingAbstract
In today's global landscape, diabetes has emerged as a significant and widespread health concern, not limited to India but affecting populations worldwide. Recent years have witnessed the onset of diabetes across all age groups, attributed to various factors such as lifestyle choices, genetic predisposition, stress, and the natural aging process. It is imperative to recognize that any trigger for diabetes can have profound implications if left undetected. In response to this growing health challenge, diverse methodologies are being deployed to predict diabetes and its associated complications. Machine learning algorithms, well-established for predictive analytics across various domains, are gaining prominence in healthcare. Although applying predictive analytics to healthcare is a complex endeavor, it holds the potential to empower healthcare professionals to make informed and timely decisions regarding patient health and treatment options. This study undertakes an investigation into predictive analytics within the healthcare domain, employing six distinct machine learning (ML) algorithms. A comprehensive dataset containing patients' clinical records is employed for testing purposes, and these six diverse ML algorithms are rigorously applied to the dataset.
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