Jul 2026· International journal of computer information systems and industrial management applications· 0 citations
TL;DR
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.
Abstract
Stroke is one of the leading causes of death and long-term disability worldwide, making early prediction and preventive healthcare extremely important. Traditional methods of stroke diagnosis mainly depend on clinical observations and medical expertise, which may sometimes delay timely identification of high-risk patients. To address this challenge, the proposed project “Stroke Prediction Using Ensemble Learning” introduces an intelligent healthcare prediction system that utilizes machine learning techniques to predict the likelihood of stroke occurrence based on patient health parameters. The system analyzes important medical attributes such as age, hypertension, heart disease, glucose level, body mass index (BMI), smoking status, and other lifestyle-related factors to provide accurate stroke risk assessment. The proposed model employs Ensemble Learning techniques, which combine multiple machine learning algorithms to improve prediction accuracy and robustness. Algorithms such as Random Forest, Gradient Boosting, AdaBoost, and Voting Classifier are integrated to enhance overall system performance and reduce the limitations of individual models. Data preprocessing techniques including missing value handling, normalization, feature selection, and class balancing are applied to improve the quality of the dataset and ensure reliable predictions. The trained ensemble model is capable of identifying complex patterns in medical data and classifying patients into stroke-risk categories with high efficiency. The system is designed with a user-friendly interface that allows healthcare professionals or users to input patient information and obtain instant prediction results. Performance evaluation metrics such as accuracy, precision, recall, F1-score, and confusion matrix are used to assess the effectiveness of the proposed model. By providing early stroke risk prediction, the system can support doctors in decision-making, promote preventive healthcare, and reduce mortality rates associated with stroke. The proposed approach demonstrates how ensemble learning and artificial intelligence can contribute significantly to modern healthcare systems by enabling faster, more accurate, and cost-effective medical predictions.
Hypertension, commonly known as high blood pressure, is a major risk factor for cardiovascular diseases and premature mortality worldwide. Early detection and prevention are critical in reducing its health impact. This study explores the application of machine learning (ML) techniques to predict the likelihood of hypertension in individuals using clinical and demographic data. A variety of supervised learning algorithms, including Logistic Regression, Random Forest, Support Vector Machines, and Gradient Boosting, were evaluated for their predictive performance [1]. The dataset was preprocessed through feature selection, normalization, and handling of missing values to improve model accuracy.[2] Performance metrics such as accuracy, precision, recall, F1-score, and AUC-ROC were used to assess the models [4]. The results demonstrate that ML models can effectively identify individuals at high risk of hypertension, offering a valuable tool for early intervention and personalized healthcare [5]. This approach underscores the potential of artificial intelligence in supporting public health efforts and enhancing clinical decision-making.
Key words: Logistic Regression, Random Forest, Support Vector Machines, and Gradient Boosting.
G. Vamsi, K. Bhargavi· International Scientific Jou...· 0 citations
The Heart Stroke Prediction System is a machine learning-based application developed to predict the risk of heart
stroke using patient health information. The system helps users identify potential stroke risks at an early stage through
intelligent data analysis. It considers important health parameters such as age, gender, hypertension, heart disease, BMI,
smoking status, glucose level, and work type. The collected data is preprocessed and analyzed to improve prediction accuracy. A
Logistic Regression algorithm is used to classify whether a person is likely to experience a stroke or not. The model is trained
using historical healthcare data and evaluated with testing datasets. The application is developed using Python and Flask to
provide a simple and user-friendly web interface. Users can enter their health details and receive instant prediction results. The
system also includes a BMI calculator and basic health guidance to improve awareness. It supports early diagnosis and
encourages preventive healthcare measures. The proposed system reduces manual assessment time and provides quick decision
support. It offers an efficient, accurate, and cost-effective solution for heart stroke risk prediction. Overall, this project
demonstrates how machine learning can assist healthcare professionals and individuals in improving early stroke detection and
promoting better health management.
G. Kumar, S. Gafur· International Journal for Re...· 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
Stroke is a significant cause of disability and death across the globe, and there is a need to have trustworthy systems of early prediction to aid in prevention, healthcare, and clinical decision-making. Even though machine learning methods have shown encouraging behaviour in terms of stroke prediction, they can usually be confined to their limits due to the presence of severe imbalance between classes in real-world healthcare data. In this paper, the authors suggest a practical framework of class-weighted machine learning to identify early stroke in a structured clinical dataset. The framework applies systematic exploratory data analysis, strong preprocessing, and model-level control of class imbalance with class-weighted learning, therefore, not relying on synthetic resampling and keeping data intact. Several supervised learning models, such as Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine, are applied and tested within a familiar experimental environment. The accuracy, weighted precision, recall, F1-score, confusion matrix analysis, and ROC-AUC are used to evaluate the performance. It is shown that with experimental results, the class-weighted Random Forest model would perform better, and the accuracy of the model is 94.2% with a weighted F1-score of 95.0% and ROC-AUC of 0.875, which is remarkable because of its discriminative ability even with data imbalance. The suggested solution offers a consistent, interpretable, and clinically viable solution to early stroke prediction risk and can be successfully incorporated into healthcare systems of decision support.
Anish Mahesh Sanga, R. Sonavane, Aaditya Anand Thokal et al.· 2026 International Conferenc...· 0 citations
There is an urgent need for explainable, clinically validated and standardised ML frameworks to translate predictive models into routine healthcare practice and improve early detection of cardiovascular disease.
Hanna Rasheed, Arya.K.R Arya.K.R, Ashida.K.A Ashida.K.A· International Journal of Tec...· 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.