Authors : Ogu Maris Uchenna; M. E. Benson-Emenike

Volume/Issue : Volume 11 - 2026, Issue 3 - March

Google Scholar : https://tinyurl.com/36fwxtpv

Scribd : https://tinyurl.com/2zj8er95

DOI : https://doi.org/10.38124/ijisrt/26mar1760

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Diabetes is one of the diseases that are causing a significant increase in the global death rate today. It causes vision loss, heart disease, foot ulcer, and stroke and kidney problems. Because of this, there must be a crucial need to compare some of this machine learning techniques to ascertain the best technique that will be of benefit to built prediction model which can be able to detect diabetes accurately on patients to enable the reduction in the numbers of people who die from the disease. Using machine learning, the research study created a comparative analysis with machine learning techniques to predict Diabetes. In this very research work, six machine learning algorithms were utilized: The Nave Bayes model (NB), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and Histogram Gradient Boosting Classifier (HBC). The models were trained in Python programming language and was evaluated on secondary dataset obtained from Kaggle http, the model's capability was evaluated by utilizing the F1 score, accuracy, recall, and R2 score. When compare to other algorithms, Random forest and Histogram Gradient Boosting Classifier had the greatest performance accuracy of 0.97%. The model was deployed on a web application after being saved with the highest accuracy using Python, Django, HTML, CSS, and other IDEs such as VS Code and Jupiter Notebook.

Keywords : Machine Learning Technique, Algorithm, Diabetes Prediction, Artificial Intelligent, Data Mining, Insulin.

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