Performance Analysis of Machine Learning Approaches in Stroke Prediction

Emon, Minhaz Uddin; Keya, Maria Sultana; Meghla, Tamara Islam; Rahman, Md. Mahfujur; Mamun, M Shamim Al; Kaiser, M. Shamim

doi:10.1109/iceca49313.2020.9297525

Cited by 116 publications

(33 citation statements)

References 18 publications

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“…Additionally, Random Forest Classifier can effectively reduce the risk of overfitting and achieve high accuracy by combining multiple decision trees. The BernoulliNB, KNeighbors, Logistic Regression, SVM, and GaussianNB models may not be as well-suited for stroke prediction as Decision Tree and Random Forest Classifier due to their sensitivity to imbalanced data, overfitting, and/or underfitting, as well as their inability to capture complex non-linear relationships among input features [9]. Additionally, these models may not be optimized for handling binary classification problems like stroke prediction, which requires accurate identification of the minority class (i.e., individuals who will have a stroke).…”

Section: Machine Learning Classifiermentioning

confidence: 99%

Stroke prediction using machine learning models

2023

International Conference on Modern Medicine and Global Health (ICMMGH 2023)

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Section: Machine Learning Classifiermentioning

confidence: 99%

Stroke prediction using machine learning models

2023

International Conference on Modern Medicine and Global Health (ICMMGH 2023)

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“…Beberapa penelitian sebelumnya telah menggunakan teknik machine learning untuk memprediksi stroke (Sailasya & Kumari, 2021;Emon et al, 2020;Shafiul Azam et al, 2020). Algoritma machine learning seperti Support Vector Machine (SVM) digunakan untuk memprediksi stroke dan menghasilkan akurasi sebesar 90% (Jeena & Kumar, 2017).…”

Section: Pendahuluanunclassified

“…Selanjutnya, Sailasya dkk melakukan analisis performa prediksi stroke menggunakan beberapa algoritma machine learning dan menghasilkan akurasi tertinggi pada algoritma Naïve Bayes sebesar 82% (Sailasya & Kumari, 2021). Penelitian (Emon et al, 2020) menunjukkan bahwa model dilatih menggunakan algoritma Decision Tree, Random Forest, Multi-layer perceptron untuk memprediksi stroke. Akurasi yang diperoleh untuk Decision Tree adalah 74,31%, Random Forest sebesar 74,53%, dan Multi-layer perceptron sebesar 75,02%.…”

Section: Pendahuluanunclassified

Penerapan Machine Learning Untuk Prediksi Penyakit Stroke

Cahyani

2022

JKMA

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Stroke is a global health problem and one of the leading causes of adult disability. Early detection and prompt treatment are needed to minimize further damage to the affected brain area and complications to other parts of the body. Machine learning techniques can be used to predict stroke detection. Machine learning algorithms such as Naive Bayes, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Decision Tree are compared in this study to obtain the best performance in predicting stroke. The implementation stages in this research consist of the pre-processing data, the application of the algorithm and the evaluation and analysis. The Naïve Bayes algorithm obtains better Accuracy, Precision, Recall, and F1-Measure values compared to other algorithms. The values of Accuracy, Precision, Recall, and F1-Measure obtained by Naïve Bayes are 93.93%, 88.23%, 93.93%, and 91.00%, respectively. So the conclusion of this study is that the Naïve Bayes algorithm has the best performance compared to the SVM, KNN and Decision Tree algorithms in predicting stroke.Keywords: decision tree, klasifikasi, k-nearest neighbor, naïve bayes, stroke, support vector machine

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“…The Cardiovascular Health Study (CHS) dataset was utilized by Singh and Choudhary [9] to predict stroke in individuals. Emon et al [10] implemented the learning-based classification algorithms namely XGboost, Random Forest, Navies Bayes, Logistic Regression and Decision Tree on the dataset which is retrieved from Kaggle. The likelihood of stroke was investigated by Kansadub et al [11] using decision trees, neural networks, and Naive Bayes analysis, and the study's authors attempted to predict strokes from the data.…”

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confidence: 99%

Prediction of Brain Stroke using Machine Learning Algorithms and Deep Neural Network Techniques

Rahman

Hasan

Sarkar

2023

EJECE

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The brain is the human body's primary upper organ. Stroke is a medical disorder in which the blood arteries in the brain are ruptured, causing damage to the brain. When the supply of blood and other nutrients to the brain is interrupted, symptoms might develop. Stroke is considered as medical urgent situation and can cause long-term neurological damage, complications and often death. The World Health Organization (WHO) claims that stroke is the leading cause of death and disability worldwide. Early detection of the numerous stroke warning symptoms can lessen the stroke's severity. The main objective of this study is to forecast the possibility of a brain stroke occurring at an early stage using deep learning and machine learning techniques. To gauge the effectiveness of the algorithm, a reliable dataset for stroke prediction was taken from the Kaggle website. Several classification models, including Extreme Gradient Boosting (XGBoost), Ada Boost, Light Gradient Boosting Machine, Random Forest, Decision Tree, Logistic Regression, K Neighbors, SVM - Linear Kernel, Naive Bayes, and deep neural networks (3-layer and 4-layer ANN) were successfully used in this study for classification tasks. The Random Forest classifier has 99% classification accuracy, which was the highest (among the machine learning classifiers). The three layer deep neural network (4-Layer ANN) has produced a higher accuracy of 92.39% than the three-layer ANN method utilizing the selected features as input. The research's findings showed that machine learning techniques outperformed deep neural networks.

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Performance Analysis of Machine Learning Approaches in Stroke Prediction

Cited by 116 publications

References 18 publications

Stroke prediction using machine learning models

Stroke prediction using machine learning models

Penerapan Machine Learning Untuk Prediksi Penyakit Stroke

Prediction of Brain Stroke using Machine Learning Algorithms and Deep Neural Network Techniques

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