Diabetes Prediction Using Support Vector Machine With Android Applications
DOI:
https://doi.org/10.48001/978-81-980647-6-9-2Keywords:
Diabetes Mellitus, Machine Learning, SVM Classifier, Healthcare, FastAPI DeploymentAbstract
Diabetes Mellitus is a critical disease affecting millions globally, with its prevalence rising alarmingly due to lifestyle changes and environmental factors. Key contributors to diabetes include age, obesity, lack of exercise, hereditary diabetes, poor dietary habits, and high blood pressure. Patients diagnosed with diabetes face a heightened risk of severe complications, including heart disease, kidney failure, and stroke, which can significantly impact their quality of life. Current diagnostic practices in healthcare often involve extensive testing, which is not only time-consuming but also resource-intensive, leading to delays in treatment. In recent years, Machine Learning (ML) and Big Data Analytics have emerged as transformative tools in healthcare, enabling the extraction of meaningful insights from vast datasets. This paper proposes a streamlined machine learning-based approach for diabetes prediction, aimed at improving accuracy and efficiency. By utilizing a Support Vector Machine (SVM) classifier, undersampling techniques for class balance, and deployment through FastAPI, this work delivers a deployable pipeline with high prediction accuracy and real-time application capabilities.
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References
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