In a recent paper [5], Mohan and Jain used the SVM algorithm to analyze and predict diabetes with the help of the Pima Indian Diabetes Dataset. This work used four types of kernels, linear, polynomial, RBF, and sigmoid, to predict diabetes in the machine learning platform.

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February 27, 2020 - In existing method, the classification and prediction accuracy is not so high. In this paper, we have proposed a diabetes prediction model for better classification of diabetes which includes few external factors responsible for diabetes along with regular factors like Glucose, BMI, Age, Insulin, etc.

January 15, 2025 - It achieved an accuracy of 96.09 %, a sensitivity of 98.54 %, and a specificity of 93.63 %. These results indicate that the KNN model with a mean test error of 0.0391 is the most reliable for predicting diabetes within the studied dataset.

July 30, 2020 - This study evaluated the performance of 24 models for diabetes prediction by using eight predictors: sex, heart disease, hypertension, smoking history, BMI (Body Mass Index), HbA1c level (Hemoglobin A1c), and blood glucose level obtained from an open data source, Kaggle. Data preparation involved curating and cleaning to ensure unbiased training and a balanced dataset before applying the dataset to machine learning training.

October 17, 2025 - To address these issues, our study proposes an integrated machine learning framework that tackles these critical limitations. Specifically, our framework combines advanced feature selection algorithms with thorough data preprocessing and class balancing techniques to build an accurate, efficient, and interpretable diabetes prediction model that can achieve better performance with even a smaller number of features used.

The purpose of this work is to detect and classify type 2 diabetes in patients using machine learning (ML) models, and to select the most optimal model to predict the risk of diabetes. In this paper, five ML models, including K-nearest neighbor (K-NN), Bernoulli Naïve Bayes (BNB), decision tree (DT), logistic regression (LR), and support vector machine (SVM), are investigated to predict diabetic patients. A Kaggle-hosted Pima Indian dataset containing 768 patients with and without diabetes was used, including variables such as number of pregnancies the patient has had, blood glucose concentration, diastolic blood pressure, skinfold thickness, body insulin levels, body mass index (BMI), genetic background, diabetes in the family tree, age, and outcome (with/without diabetes).

May 1, 2025 - Notably, algorithms such as logistic regression, Naive Bayes, and k-nearest neighbors (KNN) have shown significant potential in the prediction of type 1 or type 2 diabetes in prediabetic patients.

November 22, 2024 - Early diagnosis is paramount in ... robust framework for diabetes prediction using Synthetic Minority Over-sampling Technique (SMOTE) with ensemble machine learning techniques....

February 12, 2025 - This study presents a framework for diabetes prediction using Machine Learning (ML) models, complemented with eXplainable Artificial Intelligence (XAI) tools, to investigate both the predictive accuracy and interpretability of the predictions from ML models. Data Preprocessing is based on the Synthetic Minority Oversampling Technique (SMOTE) and feature scaling used on the Diabetes Binary Health Indicators dataset to deal with class imbalance and variability of clinical features.

January 15, 2026 - To address these challenges, researchers are increasingly turning to big data analytics and machine learning (ML) methodologies. This study presents the development of an ML-based system aimed at predicting the likelihood of diabetes and classifying its various types.

January 1, 2019 - Fig1. Taxonomy of Machine Learning Algorithms for Diabetes Prediction A.The Supervised Learning/Predictive Models Supervised learning algorithms are used to construct predictive models.

December 11, 2024 - The findings reveal that ensemble methods like Gradient Boosting and XGBoost outperform traditional models in prediction accuracy. These machine learning approaches not only enhance prediction but also identify key risk factors, aiding in early diagnosis and timely prevention of diabetes.

January 17, 2024 - Mostly, global explanations have been used in related work. In this study, the authors aimed to develop the first-ever self-explanatory interface for diagnosing diabetes using machine learning methods. This implies an interface to predict whether an individual is likely to have diabetes or not with the reasoning behind.

June 6, 2025 - Sunita Varma [18] implemented machine learning algorithms such as K-Nearest Neighbor, Logistic Regression, Decision Tree, Support Vector Machine, Gradient Boosting and Random Forest to predict diabetes.

November 4, 2023 - In this study, we propose an innovative pipeline-based multi-classification framework to predict diabetes in three classes: diabetic, non-diabetic, and prediabetes, using the imbalanced Iraqi Patient Dataset of Diabetes. Our framework incorporates various pre-processing techniques, including duplicate sample removal, attribute conversion, missing value imputation, data normalization and standardization, feature selection, and k-fold cross-validation. Furthermore, we implement multiple machine learning models, such as k-NN, SVM, DT, RF, AdaBoost, and GNB, and introduce a weighted ensemble approach based on the Area Under the Receiver Operating Characteristic Curve (AUC) to address dataset imbalance.

The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger ...

The key goals of this research were to develop and execute a method for predicting diabetes using machine learning techniques, as well as investigate the strategies that would be used to achieve success in this Endeavour. The suggested technique makes use of a wide variety of classification ...

June 25, 2024 - This project aims to develop a system that can perform an early prediction of diabetes for a patient with higher accuracy by combining the results of different machine learning techniques and integrating them with a web app so that the user can check their chances of having diabetes live.

June 13, 2025 - Objective: This study highlights the prediction capabilities of statistical and non-statistical machine learning methods over Diabetes risk classification in 768 samples from the Pima Indians Diabetes Database. It consists of the significant demographic and clinical features of age, body mass index (BMI) and blood glucose levels that greatly depend on the vulnerability against Diabetes.