Machine Learning Models for Diabetes Prediction Using Clinical Healthcare Data

Abstract

Diabetes mellitus is a prevalent non-communicable disease in millions of people throughout the world and the early detection of this diabetes can decrease the serious health complications. Standard diagnosis practices are typically labor intensive and may not be effective for effective large-scale screening practices. In this study, the use of machine learning models to predict diabetes based on clinical healthcare data is presented. Multiple machine learning algorithms such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN) were implemented and evaluated. To enhance the accuracy of prediction, the pre-processing technique, namely normalization, handling missing values and feature selection were applied to the dataset. Various parameters such as accuracy, precision, recall, F1-score, confusion matrix and ROC-AUC were used for performance evaluation. The experimental results showed that ANN and RF models performed well in predicting the results of the experiments than other algorithms. The highest accuracy and classification efficiency for identifying diabetic patients was obtained by the ANN model. The results show the potential of machine learning methods in assisting early diagnosis of diabetes and intelligent healthcare decision making systems. The framework could be highly successful in enhancing preventive healthcare and lessen the burden of medical professionals by leveraging automated disease prediction systems.

Introduction

The text discusses a machine learning-based approach for early prediction of diabetes mellitus, a widespread chronic disease that causes serious complications such as cardiovascular disease, kidney failure, and blindness if not detected early.

It first explains that traditional diagnostic methods (blood tests and clinical exams) are effective but often slow and costly for large-scale early screening. As a result, machine learning (ML) and artificial intelligence (AI) have emerged as promising alternatives because they can analyze large clinical datasets and identify hidden patterns for disease prediction.

The study focuses on using clinical data (especially the Pima Indians Diabetes Dataset) and applies multiple machine learning models such as Logistic Regression, Decision Tree, Random Forest, SVM, KNN, and Artificial Neural Networks (ANN). These models use medical features like glucose level, BMI, insulin, blood pressure, age, and family history to predict whether a person is diabetic.

The methodology includes data preprocessing (handling missing values, normalization), feature selection, model training (80% training / 20% testing split), and evaluation using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. The implementation is done using Python libraries like Scikit-learn, TensorFlow, and Keras.

Literature review shows that earlier statistical methods were limited, while modern ML and deep learning methods (especially ensemble models like Random Forest and ANN) significantly improve prediction accuracy. However, challenges like data imbalance and interpretability still exist.

In the results, the Artificial Neural Network (ANN) performs best, achieving about 94% accuracy and 0.97 ROC-AUC, followed closely by Random Forest (92%). Logistic Regression performs the weakest due to its limitation in handling complex nonlinear relationships.

Conclusion

In this study, machine learning models for diabetes prediction were successfully developed and evaluated with clinical data from healthcare. Different algorithms such as Logistic Regression, Decision Tree, Random Forest, SVM, KNN and ANN were used and compared by various performance evaluation criteria. Experimental results showed that the ANN and RF models were more accurate and more efficient in terms of classification than the traditional machine learning models. The proposed framework proved the efficiency of the machine learning techniques in early diabetes diagnosis and intelligent healthcare decision making systems. This combination of preprocessing techniques, feature selection methods, and powerful learning algorithms enhanced the accuracy and efficiency of the predictions. The developed system may assist health care professionals to detect patients at an early stage of high risk and in preventive health care management. Although the study yielded positive outcomes, there are some drawbacks such as the fact that the data set is not very diverse, class imbalance, and reliance on historical clinical records. For future, larger real-time healthcare datasets, hybrid deep learning techniques, explainable artificial intelligence, and IoT-based healthcare monitoring systems can be utilized in the prediction accuracy and practical implementation in the clinical context.

References

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Copyright

Copyright © 2026 Aakash Sunil Chaudhari. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.