A Machine Learning Ensemble Classifier for Prediction of Brain Strokes

Mostafa, Samaa A.; Elzanfaly, Doaa S.; Yakoub, Ahmed Elsayed

doi:10.14569/ijacsa.2022.0131232

Cited by 11 publications

(2 citation statements)

References 18 publications

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“…Effectively handling the presence of missing values in the data KNN [63], [80], [202] algorithm The simplest classification method Naive Bayes [80], [130], [202], [203] var_smoothing: 0.0533…”

Section: Disease Machine Learning Algorithm Parameter Used Reasons Th...mentioning

confidence: 99%

Chronic Diseases Prediction Using Machine Learning With Data Preprocessing Handling: A Critical Review

Ghaniaviyanto Ramadhan,

Adiwijaya,

Maharani

et al. 2024

IEEE Access

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According to the World Health Organization (WHO), some chronic diseases such as diabetes mellitus, stroke, cancer, cardiac vascular, kidney failure, and hypertension are essential for early prevention. One of the prevention that can be taken is to predict chronic diseases using machine learning based on personal medical record or general checkup result. The common prediction objective is to minimize the prediction error as low as possible. The most influencing chronic diseases prediction factors are the quality of data and the choice of predictor such as machine learning methods. The five main problems those lower data quality are outliers, missing values, feature selection, normalization, and imbalance. After we ensure the quality of data, the next task is to choose the best machine learning methods. The most influencing factor to consider when we choose the predictor its performance evaluation (accuracy, recall, precision, f1-score). Thus, predicting chronic disease aims to produce increased performance and solve problems in medical data. This paper presents a Systematic Literature Review (SLR) that offers a comprehensive discussion of research on chronic diseases prediction using machine learning and its data preprocessing handling. This paper covers machine learning methods discussion such as supervised learning, ensemble learning, deep learning, and reinforcement learning. The preprocessing handling we discuss includes missing values, outliers, feature selection, normalization, and imbalance. The final discussions of this paper are open issues, and the potential future works in improving the prediction performance for chronic diseases using a data preprocessing handling and machine learning methods.

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Section: Disease Machine Learning Algorithm Parameter Used Reasons Th...mentioning

confidence: 99%

Chronic Diseases Prediction Using Machine Learning With Data Preprocessing Handling: A Critical Review

Ghaniaviyanto Ramadhan,

Adiwijaya,

Maharani

et al. 2024

IEEE Access

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“…Mostafa SA et al [10] in their studies utilized stacking techniques for stroke prediction. They also employed smote and standarization for data balancing and scaling respectively.…”

Section: Related Workmentioning

confidence: 99%

Classifier Comparison for Stroke Prediction Ensembling SMOTE+ENN using Machine Learning Approach

et al. 2023

Preprint

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Stroke is one of the deadliest diseases found in the world which is the second major reason for mortality rate. Early detection of stroke can reduce the mortality due to stroke. Inorder to diagnose it earlier several machine learning techniques are being utilized. This proves that machine learning can also be used for disease prediction for various diseases. Supervised machine learning algorithms has been used for stroke prediction. Important feature responsible for stroke prediction has been done. Inorder to balance the dataset hybrid sampling technique of SMOTE + ENN has been performed so that the results are much appreciable. After sampling, machine learning models has been used for stroke prediction using Logistic Regression (LR), KNearest Neighbour (KNN), Naïve Bayes (NB), Decision Tree (DT), Support Vector Classifier (SVC) and Random Forest (RF). With all the models Random Forest achieved higher performance with accuracy of 99%, recall of 100%, precision of 98% and f-score of 99%. The proposed work also extends the use of different classifiers like Voting, Bagging and Stacking methods. Among all the proposed classifiers stacking provides higher performance with all metrics. The stacking classifer achieved accuracy of 100%, recall of 100%, precision of 99% and f-score of 100%. From the results, it is known that Random forest algorithm perform better with data sampling of SMOTE + ENN than other models.

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