A predictive analytics approach for stroke prediction using machine learning and neural networks

Dev, Soumyabrata; Wang, Hewei; Nwosu, Chidozie Shamrock; Jain, Nishtha; Veeravalli, Bharadwaj; John, Deepu

doi:10.1016/j.health.2022.100032

Cited by 107 publications

(34 citation statements)

References 13 publications

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“…Furthermore, the models trained on reduced feature set also consumed less computational time i.e only 3.86 iterations per second(it/s) using CVD and 15.52 iterations per second(it/s) using framingham dataset. We have also validated our findings by comparing our work with other published proposals [41,42] where same datasets were used with full feature set and the obtained accuracy results were less or equal to the results that we obtained using reduced feature set. Overall, the experimental results proved that the performance of the ML models increased significantly by using only the relevant features.…”

Section: Classification Results Using Full Feature Setsupporting

confidence: 77%

Analyzing the impact of feature selection on the accuracy of heart disease prediction

Pathan¹,

Nag²,

Pathan³

et al. 2022

Preprint

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Heart Disease has become one of the most serious diseases that has a significant impact on human life. It has emerged as one of the leading causes of mortality among the people across the globe during the last decade. In order to prevent patients from further damage, an accurate diagnosis of heart disease on time is an essential factor. Recently we have seen the usage of noninvasive medical procedures, such as artificial intelligence-based techniques in the field of medical. Specially machine learning employs several algorithms

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Section: Classification Results Using Full Feature Setsupporting

confidence: 77%

Analyzing the impact of feature selection on the accuracy of heart disease prediction

Pathan¹,

Nag²,

Pathan³

et al. 2022

Preprint

Self Cite

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“…As explained in [19], the authors proposed a comprehensive assessment of patient characteristics in the digital health record. They used systematic analysis to look at various features.…”

Section: Literature Reviewmentioning

confidence: 99%

A-Tuning Ensemble Machine Learning Technique for Cerebral Stroke Prediction

et al. 2023

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A cerebral stroke is a medical problem that occurs when the blood flowing to a section of the brain is suddenly cut off, causing damage to the brain. Brain cells gradually die because of interruptions in blood supply and other nutrients to the brain, resulting in disabilities, depending on the affected region. Early recognition and detection of symptoms can aid in the rapid treatment of strokes and result in better health by reducing the severity of a stroke episode. In this paper, the Random Forest (RF), Extreme Gradient Boosting (XGBoost), and light gradient-boosting machine (LightGBM) were used as machine learning (ML) algorithms for predicting the likelihood of a cerebral stroke by applying an open-access stroke prediction dataset. The stroke prediction dataset was pre-processed by handling missing values using the KNN imputer technique, eliminating outliers, applying the one-hot encoding method, and normalizing the features with different ranges of values. After data splitting, synthetic minority oversampling (SMO) was applied to balance the stroke samples and no-stroke classes. Furthermore, to fine-tune the hyper-parameters of the ML algorithm, we employed a random search technique that could achieve the best parameter values. After applying the tuning process, we stacked the parameters to a tuning ensemble RXLM that was analyzed and compared with traditional classifiers. The performance metrics after tuning the hyper-parameters achieved promising results with all ML algorithms.

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“…Sistem klasifikasi dan diagnosis pada suatu penyakit dengan menggunakan algoritma Jaringan Syaraf Tiruan (JST) sebagai salah satu cabang Artificial Intelligence (AI) direkomendasikan oleh Najeeb Abbas A. dkk. [8], yang melakukan penelitian tentang keandalan JST perambatan balik sebagai salah satu metode klasifikasi dengan tingkat akurasi tinggi [9].…”

Section: Tinjauan Literaturunclassified

Sistem Identifikasi Dini Penyakit Stroke Dengan Menggunakan Jaringan Syaraf Tiruan Perambatan Balik

Putra¹,

Kusumawardhani²,

Nugraheni³

et al. 2022

JTI

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Heart disease is a disease with the second-highest mortality rate in the world. This happens because of an unhealthy human lifestyle. This unhealthy lifestyle affects the performance of the body's organs in carrying out their functions. Stroke can be prevented by exercising regularly, eating nutritious foods, not consuming alcohol, and not consuming tobacco. One way to find out if someone is free from stroke or not can be done by medical check-ups. However, this method is quite expensive. Given these problems, this study aims to design an early identification system for detecting early-stage stroke. The system is designed by utilizing the condition and history of the subject for identification. This study uses a back propagation neural network for the classification process. Variations in the use of hidden layers in each experiment were used to obtain the highest accuracy in the training process. From the results of the study, it was found that the system designed can detect early stroke with an accuracy rate of 97.8%.

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A predictive analytics approach for stroke prediction using machine learning and neural networks

Cited by 107 publications

References 13 publications

Analyzing the impact of feature selection on the accuracy of heart disease prediction

Analyzing the impact of feature selection on the accuracy of heart disease prediction

A-Tuning Ensemble Machine Learning Technique for Cerebral Stroke Prediction

Sistem Identifikasi Dini Penyakit Stroke Dengan Menggunakan Jaringan Syaraf Tiruan Perambatan Balik

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