Jul 2026· International Journal of Healthcare and Information Technology· Vol 4, pp. 26-34· 0 citations
TL;DR
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.
Abstract
Stroke is a major global health problem that may lead to permanent disability or death. In Indonesia, analytical approaches such as data mining have been increasingly used to support early identification of stroke risk based on patient health records. This study applies the Naive Bayes algorithm as the main classification method. The research procedure includes data collection from the Kaggle repository, data selection based on predetermined clinical criteria, data cleaning to remove duplicates and missing values, and data transformation by converting categorical attributes into numerical form. The dataset was then split into training and testing subsets for model development. The final dataset consisted of 3,256 patient records containing variables such as gender, age group, hypertension, heart disease, average glucose level, body mass index (BMI), smoking status, and stroke occurrence. After completing these preprocessing stages, the Naive Bayes model achieved an accuracy of 89.49%. This result indicates that the model was able to classify stroke and non-stroke cases in the dataset with a satisfactory level of accuracy. The findings suggest that the model may serve as a baseline component for developing decision-support systems aimed at facilitating early identification of stroke risk in targeted population groups.
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.
Stroke is one of the leading causes of death and disability worldwide, requiring an accurate machine learning-based risk prediction approach to support early detection. This study aims to conduct a comparative evaluation of three supervised learning algorithms, namely Naïve Bayes, Random Forest, and SVM, in predicting stroke risk. The clinical dataset used consisted of 5,110 patients. Model evaluation was performed using the Stratified 5-Fold Cross Validation method, a cross-validation technique that divides the data into five subsets while maintaining the class proportions in each fold. Each subset is alternately used as test data, while the other subset is used as training data, so that all data can be used as training data and test data. Model performance was measured using accuracy, confusion matrix, and AUC-ROC metrics to assess classification performance. The results showed that Random Forest achieved the best performance with an accuracy of 95%, followed by Naïve Bayes at 86% and SVM at 75%. Based on the AUC-ROC evaluation, Random Forest also showed the most optimal performance with a value of 0.80, indicating excellent classification ability. Random Forest is the most effective algorithm in predicting stroke risk in the dataset used, so it has the potential to be the best method used to support the early stroke detection system.
Alya Masitha, H. M. Jumasa, Wellie Sulistijanti et al.· Rabit : Jurnal Teknologi dan...· 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
Stroke is a leading cause of death and disability worldwide, and early risk prediction is essential for reducing its public health burden. In this paper, six machine learning models were developed and thoroughly compared. An analysis of stroke risk using the publicly available Kaggle Stroke data set. The article discusses the Prediction Dataset and the use of Logistic Regression, Random Forest, and Support Vector methods. The performance of Machine, K-Nearest Neighbors, Decision Tree, and XGBoost was assessed by means of the Accuracy, Precision, Recall, F1-score, and AUC-ROC metrics. Because class weighting was used to deal with the severe data imbalance, it was found that. Since Logistic Regression, Random Forest, and XGBoost all gave AUC-ROC values above 0.80, they therefore meet the clinically acceptable threshold for effectiveness. Since regression gave the highest Recall of 0.84, it is clear that regression is superior. The performance of identifying high-risk patients is discussed in connection with feature importance. From the analysis it was clearly established that age, average glucose level, and body mass index were the most important predictors, hence the study properly validates this. The paper discusses the feasibility of using machine learning for stroke risk prediction and therefore gives a very useful reference for clinical auxiliary screening.
The proposed model employs Ensemble Learning techniques, which combine multiple machine learning algorithms to improve prediction accuracy and robustness, and is capable of identifying complex patterns in medical data and classifying patients into stroke-risk categories with high efficiency.
Bhagyashri Patil, Priyadarshini C Patil, Soumya M A et al.· International journal of com...· 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