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Predicting Human Diabetes with Machine Learning

Rahul R U, S. Kiran Reddy, Tippesh B H, Vishal Raja S U, Dr. Deepak N R, Omprakash B

Abstract


This study presents a machine learning based model for predicting diabetes onset, leveraging the PIMA Indian Diabetes Dataset. The project explored Commonly used algorithms include Logistic Regression, Decision Trees, Random Forests, and Support Vector Machines (SVM).to assess their predictive capabilities. Extensive data preprocessing and feature selection were employed to enhance model accuracy. Among the tested models, Random Forest exhibited the highest performance with an accuracy of 89 percent, demonstrating its robustness for clinical application.


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References


Pima Indians Diabetes Database (UCI Machine Learning Repository) This database includes dataset that is really useful in diabetes prediction.

National Health and Nutrition Examination Survey (NHANES)- It has diabetes-related variables such as glucose levels and HbA1c measurements.

Kaggle Diabetes Datasets- hosts various diabetes-related datasets that include features like age, glucose levels, insulin, BMI, and lifestyle factors.

World Health Organization (WHO) Diabetes Data- global diabetes statistics and countryspecific prevalence data. While not a machine learning dataset, this information can complement predictive modeling.

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