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Abstract

As technology progresses, data grows exponentially and is produced quickly from a wide variety of sources. Data storage, analysis, and interpretation become challenging due to data's variety and complexity. Despite the fact that we can now store such enormous amounts of data, much progress has to be made in terms of analyzing and making sense of that data. Better clinical routes to detect and predict diabetes at early stages are essential for reducing complications and delaying the onset of diabetes. The worldwide rise in the prevalence of diabetes is one of the most concerning trends in modern medicine. A high blood glucose level, caused by insufficient insulin synthesis or improper insulin response by body cells, characterizes the metabolic illnesses known together as diabetes mellitus. Organs as diverse as the brain, nervous system, heart, kidney, eyes, skin, and limbs may all suffer harm from diabetes mellitus. In this study, we used machine learning to differentiate between type 1 and type 2 diabetes trends

Keywords

analysis techniques classification machine learning

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Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

How to Cite
Ban Hamid Ali, & Dr. Mahmoud koabaz. (2023). Developing a ML-Based Model for Detecting Diabetes. Texas Journal of Engineering and Technology, 23, 1–10. Retrieved from https://zienjournals.com/index.php/tjet/article/view/4281

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