Chi-Squared Based Feature Selection for Stroke Prediction using AzureML

Ray, Sujan; Alshouiliy, Khaldoon; Roy, Anupam Singha; AlGhamdi, Ali Saad Thafeed; Agrawal, Dharma P.

doi:10.1109/ietc47856.2020.9249117

Cited by 23 publications

(16 citation statements)

References 1 publication

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“…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%

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: Evaluation Methodsmentioning

confidence: 99%

Machine Learning Prediction of Stroke Occurrence: A Systematic Review

Lolak,

Suppasilp,

Poprom

et al. 2024

Preprint

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“…As a result, select the number of features based on the highest chi-squared scores. The chi-squared formula is presented below [16]:…”

Section: Chi-squaredmentioning

confidence: 99%

A Survey of Machine Learning Approaches Applied to Gene Expression Analysis for Cancer Prediction

et al. 2022

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Machine learning approaches are powerful techniques commonly employed for developing cancer prediction models using associated gene expression and mutation data. Our survey provides a comprehensive review of recent cancer studies that have employed gene expression data from several cancer types (breast, lung, kidney, ovarian, liver, central nervous system and gallbladder) for survival prediction,tumor identification and stratification. We also provide an overview of biomarker studies that are associated with these cancer types. The survey captures multiple aspects of machine learning associated cancer studies,including cancer classification, cancer prediction, identification of biomarker genes, microarray, and RNA-Seq data. We discuss the technical issues with current cancer prediction models and the corresponding measurement tools for determining the activity levels of gene expression between cancerous tissues and noncancerous tissues. Additionally, we investigate how identifying putative biomarker gene expression patterns can aid in predicting future risk of cancer and inform the provision of personalized treatment.

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“…The chi-squared test was proposed [22] for feature selection to extract the top six features to train two ML models, that is, a two-class decision jungle model and a two-class boosted decision tree models. These two ML models were trained with all features and the top six features.…”

Section: Related Workmentioning

confidence: 99%

Performance analysis and comparison of various machine learning algorithms for early stroke prediction

2022

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Stroke is the leading cause of permanent disability in adults, and it can cause permanent brain damage. According to the World Health Organization, 795 000 Americans experience a new or recurrent stroke each year. Early detection of medical disorders, for example, strokes, can minimize the disabling effects. Thus, in this paper, we consider various risk factors that contribute to the occurrence of stoke and machine learning algorithms, for example, the decision tree, random forest, and naive Bayes algorithms, on patient characteristics survey data to achieve high prediction accuracy. We also consider the semisupervised self‐training technique to predict the risk of stroke. We then consider the near‐miss undersampling technique, which can select only instances in larger classes with the smaller class instances. Experimental results demonstrate that the proposed method obtains an accuracy of approximately 98.83% at low cost, which is significantly higher and more reliable compared with the compared techniques.

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Chi-Squared Based Feature Selection for Stroke Prediction using AzureML

Cited by 23 publications

References 1 publication

Machine Learning Prediction of Stroke Occurrence: A Systematic Review

Machine Learning Prediction of Stroke Occurrence: A Systematic Review

A Survey of Machine Learning Approaches Applied to Gene Expression Analysis for Cancer Prediction

Performance analysis and comparison of various machine learning algorithms for early stroke prediction

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