Abstract
Diabetes mellitus is a common chronic metabolic disease which, if not diagnosed, can cause a host of serious
diseases like cardiovascular diseases, kidney failure, nerve damage, etc. Timely and accurate diagnosis can help
to save treatment costs and enhance treatment outcomes for patients with diabetes. In this project, a Machine
Learning based diabetes prediction system is developed on the basis of the Pima Indian Diabetes dataset which
contains clinical parameters like Glucose level, Blood pressure, BMI, Insulin level, Age, Heart disease,
Smoking history. The pre-processed data is used to implement, train and test three supervised classification
algorithms: Decision Tree, Random Forest, and Gradient Boosting. In order to compare the models, their
accuracy is chosen as the primary performance measurement. The results of the experiments indicate that the
Gradient Boosting model has the highest accuracy of 97%, followed by Random Forest with 90% accuracy and
Decision Tree with 86% accuracy. The results show that ensemble and boosting based methods significantly
outperform simple tree-based classifiers for this prediction task, which makes Gradient Boosting the most
appropriate model to be used with a real-world diabetes risk assessment tool.
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