The results indicate that the Gaussian Naive Bayes algorithm is capable of providing highly accurate classification results on the simulated dataset used in this study, and has the potential to serve as a supporting approach for the identification and classification of diabetes mellitus risk.
Abstract
Diabetes mellitus is a chronic disease with a number of cases that tends to increase over time andcan lead to various complications if it is not recognized and treated at an early stage. In thiscontext, the application of data mining techniques can be used to identify and classify the risklevel of diabetes based on patients’ characteristics, thereby supporting a more effective decisionmaking process. This study applies the Gaussian Naive Bayes algorithm to classify the riskof diabetes mellitus using 200 simulated data records representing the characteristics of patients medical records. The data consist of several attributes, including age, gender, body weight, height, Body Mass Index (BMI), blood pressure, glucose level, cholesterol level, physical activity, family history of diabetes, smoking habits, and diabetes status as the target variable. The research process consists of several stages, beginning with Exploratory Data Analysis (EDA) to understand the characteristics of the dataset, followed by data preprocessing, conversion of categorical data using the Label Encoding method, and division of the dataset into training and testing sets using an 80:20 ratio. Subsequently, a classification model was developed using theGaussian Naive Bayes algorithm, and its performance was evaluated using several evaluation metrics, including Accuracy, Precision, Recall, F1-Score, Confusion Matrix, and Classification Report. Based on the testing results, the model achieved an Accuracy of 97.50%, Precision of 94.74%, Recall of 100%, and F1-Score of 97.30%. These results indicate that the Gaussian Naive Bayes algorithm is capable of providing highly accurate classification results on the simulated dataset used in this study. Therefore, the algorithm has the potential to serve as a supporting approach for the identification and classification of diabetes mellitus risk.
The findings suggest that the Naive Bayes model may serve as a baseline component for developing decision-support systems aimed at facilitating early identification of stroke risk in targeted population groups.
Nur Aggun S., Nadhifa Dwi Rahmalia, Nurrahma Harris et al.· International Journal of Hea...· 0 citations
Diabetes is a chronic metabolic disease characterized by high blood glucose levels that can lead to severe complications affecting various organs. The increasing prevalence of diabetes requires an accurate and efficient diagnostic approach to support early detection and medical decision-making. The development of machine learning technology provides opportunities to assist disease diagnosis by analyzing patient health data and identifying patterns associated with diabetes conditions. This study aims to compare the accuracy performance of the Naive Bayes and CatBoost algorithms in diagnosing diabetes using the confusion matrix evaluation method. The research method consists of several stages, including data collection, data preprocessing, model selection, model training, and model evaluation. The dataset used was obtained from Kaggle, consisting of 768 records with nine attributes, including pregnancies, glucose level, blood pressure, skin thickness, insulin, body mass index, diabetes pedigree function, age, and outcome classification. The models were trained using 80% training data and 20% testing data. The evaluation results showed that the Naive Bayes algorithm achieved an accuracy of 79%, while the CatBoost algorithm obtained a higher accuracy of 81%. These findings indicate that CatBoost provides better classification performance compared to Naive Bayes for diabetes diagnosis in this dataset. Therefore, CatBoost can be considered a more effective machine learning approach to support diabetes prediction. Future research is recommended to utilize larger datasets, optimize model parameters, and explore other advanced algorithms to improve diagnostic accuracy.
Basyach Aryodito Tranggono Heryanto, Onny Marleen· Journal of social research· 0 citations
Diabetes affects over 101 million people in India, with many more at risk due to routine and hereditary factors. Early diagnosis is crucial to prevent complications, which make accurate predictive tools essential in healthcare. This research uses Machine Learning (ML) algorithms to evaluate the likelihood of Type 2 Diabetes Mellitus (T2DM) using lifestyle and family history data. The trained models demonstrate strong predictive ability, allowing individuals to self-assess their risk and supporting healthcare professionals in early detection and intervention. This study presents a performance assessment of seven ML classifiers: Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), k-Nearest Neighbor (k-NN), and Extreme Gradient Boosting (XGBoost). These classifiers were applied to the widely used PIMA Indian Diabetes dataset (PIDD), which contains 768 clinical records of adult women aged 21 and above, providing key medical information for diabetes analysis. Multiple evaluation measures were applied to assess model performance with results showing that SVM achieved the highest accuracy and AUC, while LR, RF, and XGBoost also performed competitively. Although k-NN attained the highest recall, it yielded a higher false positive rate. These findings highlight that no single model is perfect for every situation, and the choice of classifier should match clinical needs. This study serves as a reference for ML applications in diabetes prediction.
Rizwan Akhtar, Muhammad Kalamuddin Ahamad· ITEGAM- Journal of Engineeri...· 0 citations
Experimental results demonstrate that Machine Learning techniques can effectively predict disease occurrence with high accuracy, thereby assisting healthcare professionals in early diagnosis and treatment planning.
Sunidhi, Mothe Rahul, M. Kumar et al.· International Journal for Re...· 0 citations
Diabetes Mellitus (DM) is a chronic metabolic disease that continues to increase globally and requires effective early detection to reduce the risk of serious complications. Machine learning has been widely adopted as an approach for predicting diabetes risk; however, most existing models are still black-box in nature, making them difficult to interpret and less useful for clinical decision-making. In addition, the problem of class imbalance in medical datasets often causes models to be biased toward the majority class, reducing their sensitivity in detecting high-risk patients. This study aims to develop and compare diabetes risk prediction models using the Naive Bayes and Support Vector Machine (SVM) algorithms with an Explainable Artificial Intelligence (XAI) approach. Class imbalance was addressed using the Synthetic Minority Over-sampling Technique (SMOTE) applied to the training data. Interpretability was analyzed using SHAP (SHapley Additive exPlanations) for global feature importance and LIME (Local Interpretable Model-agnostic Explanations) for local instance-level explanations. The dataset used was the Diabetes Health Indicators Dataset from the BRFSS 2015 survey, publicly available on Kaggle, with a sample of 50,000 records and 22 variables. Evaluation results showed that SVM achieved an accuracy of 84.17%, while Naive Bayes achieved a higher recall of 77.76%, indicating better sensitivity in detecting diabetes cases. SHAP analysis identified GenHlth, HighBP, BMI, HighChol, and Age as the most influential risk factors globally, while LIME provided individual-level explanations. This research contributes a prediction model that is not only accurate but also transparent and clinically interpretable.
Putu Hawariyah, Sudin Saepudin, Gina Syabani Yuda· International Journal of Hea...· 0 citations
The experimental results show that for the GNB model, the best performance was achieved using the combination of StandardScaler, SMOTE, and SelectKBest (k=5), reaching an accuracy of 94.53%, precision 98.36%, recall 90.91%, and f1-score 94.49%.
Joshua Roy Danna Lacanlale, Vitri Tundjungsari· International Journal of Sci...· 0 citations
