A Study of Features Affecting on Stroke Prediction Using Machine Learning

Songram, Panida; Jareanpon, Chatklaw

doi:10.1007/978-3-030-33709-4_19

Cited by 5 publications

(4 citation statements)

References 9 publications

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Section: Resultsmentioning

confidence: 99%

“…In terms of evaluation metrics, most studies used common evaluation metrics for risk prediction model, with C-statistics or area under ROC curve, sensitivity, specificity, and accuracy; while fewer reported precision, F-score. 21,22,26,27,32,34,36,39,41,42 A summary of the evaluation metrics is described in Supplemental Table 5. The boosting algorithm provided the best overall median C-statistic of 0.92 (IQR: 0.90-0.95), followed by SVM [median C-statistic= 0.85 (IQR: 0.74-0.94)] and NN [median C-statistic= 0.78 (IQR: 0.75-0.91)].…”

Section: Evaluation Methodsmentioning

confidence: 99%

“…Hence, the most common structural features used were age, gender, hypertension, diabetes mellitus, smoking, alcohol consumption, history of CVD, body mass index (BMI), and blood sugar. Four studies used forward/backward eliminations, [31][32][33][34] six studies used principal component analysis (PCA), 23,24,[35][36][37][38] four studies used information gain, 28,[38][39][40] and three studies used the Chi-Square test to select the relevant risk factors or features 27,41,42 ; and the rest used various methods, e.g., correlation, least absolute shrinkage and selection operator (LASSO), specific algorithm, and medical knowledge from standard guidelines, etcetera.…”

Section: Feature Selection and Feature Engineeringmentioning

confidence: 99%

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Machine Learning Prediction of Stroke Occurrence: A Systematic Review

Lolak,

Suppasilp,

Poprom

et al. 2024

Preprint

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Background: Machine learning is nowadays commonly used for disease prediction, including cardiovascular disease. There is growing evidence of the effectiveness of machine learning algorithms for stroke risk prediction models. Aims: A systematic review was conducted to identify and comprehensively evaluate the available evidence. Summary of review: Relevant studies were identified from the three electronic databases (i) MEDLINE via Pubmed, (ii) Scopus, and (iii) IEEE Xplore from inception to 1st December 2020. Out of 12,626 studies identified, 40 used machine learning for ischemic or hemorrhagic stroke risk prediction models. Synthesis without meta-analysis identified that a boosting algorithm (median C-statistics = 0.9 (interquartile range [IQR]: 0.88-0.92)), and neural network (median C-statistic = 0.80 (IQR: 0.77-0.92)) performed best among ML models in the low risk of bias studies. Moreover, a boosting algorithm also performed best in overall (both low and high risk of bias) studies (median C-statistic = 0.92 (IQR: 0.90-0.95)). Conclusions: The systematic review found promising results of the ML algorithm model performances compare with the gold standard conventional models, such as FSRP (C-statistic 0.653) and revised FSRP (C-statistic 0.716). In term of the algorithm, boosting and neural networks are robust, but are considered as black-box models, since they are composed of non-linearity and complex algorithms. It remains questionable whether a physician would adapt these algorithms to use in a real clinical setting. Moreover, less than half of the studies (16 out of 40) were at low risk of bias in our systematic review. More researches with good methodology and study design, alongside explainable and good performance models, may become available in the future.

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Section: Resultsmentioning

confidence: 99%

Section: Evaluation Methodsmentioning

confidence: 99%

Section: Feature Selection and Feature Engineeringmentioning

confidence: 99%

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Machine Learning Prediction of Stroke Occurrence: A Systematic Review

Lolak,

Suppasilp,

Poprom

et al. 2024

Preprint

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“…Perhaps most famously, algorithms trained to read chest X-rays with near-human accuracy have been widely publicized, though have also faced some criticism (9)(10)(11). Within the subspecialty of neuroradiology, many algorithms have been or are being developed for the purposes of automated segmentation, aneurysm detection, and stroke diagnosis and prognosis, among many others (3,(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26). Much less common are applications of AI into pediatric radiology and, even more rare, pediatric neuroradiology.…”

Section: Impact Of Ai Applicationsmentioning

confidence: 99%

Current Perspectives of Artificial Intelligence in Pediatric Neuroradiology: An Overview

et al. 2021

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Artificial Intelligence, Machine Learning, and myriad related techniques are becoming ever more commonplace throughout industry and society, and radiology is by no means an exception. It is essential for every radiologists of every subspecialty to gain familiarity and confidence with these techniques as they become increasingly incorporated into the routine practice in both academic and private practice settings. In this article, we provide a brief review of several definitions and techniques that are commonly used in AI, and in particular machine vision, and examples of how they are currently being applied to the setting of clinical neuroradiology. We then review the unique challenges that the adoption and application of faces within the subspecialty of pediatric neuroradiology, and how these obstacles may be overcome. We conclude by presenting specific examples of how AI is currently being applied within the field of pediatric neuroradiology and the potential opportunities that are available for future applications.

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