Enhancing Diabetes Prediction in Health Informatics Through Software analysis and Machine Learning Synergy
DOI:
https://doi.org/10.63682/jns.v14i31S.6592Abstract
The research introduces a way to combine software engineering and machine learning to assist in predicting diabetes in health facilities. It uses step-by-step software design to create a powerful model that looks at patient records. With machine learning, we can identify what indicates a higher risk of diabetes. Health data such as age, blood pressure, levels of insulin and body mass index are incorporated in the model. Before starting the training process, missing values are identified and cleaned as needed and features are scaled. The machine learning method used in this study is the Extreme Learning Machine (ELM) which rapidly classifies patients into diabetic and non-diabetic categories. Results from the tests indicate that the model’s accuracy is almost 93%. It is also capable of identifying whether a person is diabetic or healthy which is confirmed by using evaluation techniques like the confusion matrix and ROC curve. This research reveals that it is more accurate to use a combination of software engineering and machine learning to predict diabetes. This approach can also benefit other areas of health care by leading to earlier detection of problems and better care for patients. This research aims to ensure the model is convenient, efficient, and accurate for practical use in health systems. Overall, this approach makes it easier to develop health tools with the help of widely used software and data science.
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Copyright (c) 2025 S. Vijayanand, M Sakthivel, Rachakonda Venkatesh, Anantha Raman G R, S Manivannan, K. Shanthi Latha
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