Abstract
Diabetes mellitus, characterized as a chronic metabolic condition, presents a notable global health concern. Timely detection and intervention play a crucial role in the effective management and enhancement of patient outcomes. This research paper explores the application of logistic regression as a predictive tool for diabetes diagnosis. Leveraging a substantial dataset containing clinical and patient-related variables, our study demonstrates the feasibility and efficacy of logistic regression pinpoint individuals susceptible to developing diabetes. By analyzing relevant features, and calculating the sigmoid function, cost function, and gradient descent from scratch and employing an optimal threshold, the logistic regression model exhibits commendable accuracy, sensitivity, and specificity. These findings highlight its potential as an early diagnostic tool. Such predictive models hold promise for healthcare practitioners, enabling them to proactively identify high-risk individuals and initiate preventive measures. As a cost-effective and accessible method, logistic regression aids in the early diagnosis and management of diabetes, ultimately leading to enhanced healthcare strategies and patient care.
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Acknowledgement
Our heartfelt appreciation goes out to Prof. Zarinabegam Mundargi, whose unwavering guidance and invaluable insights greatly enriched the development of this project. We also extend our gratitude to Vishwakarma Institute of Technology for their generous provision of essential resources, enabling us to bring this project to life.
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Mundargi, Z., Dabade, M., Chindhe, Y., Bondre, S., Chaudhary, A. (2024). Diabetes Prediction Using Logistic Regression. In: Gundebommu, S.L., Sadasivuni, L., Malladi, L.S. (eds) Renewable Energy, Green Computing, and Sustainable Development. REGS 2023. Communications in Computer and Information Science, vol 2081. Springer, Cham. https://doi.org/10.1007/978-3-031-58607-1_4
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DOI: https://doi.org/10.1007/978-3-031-58607-1_4
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