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. 2024 Aug 31;24(1):306.
doi: 10.1186/s12883-024-03818-6.

Development of machine learning-based models for predicting risk factors in acute cerebral infarction patients: a clinical retrospective study

Changqing Yang #  1   2 , Renlin Hu #  3 , Shilan Xiong  4   5 , Zhou Hong  3 , Jiaqi Liu  6 , Zhuqing Mao  7 , Mingzhu Chen  8   9
Affiliations

Development of machine learning-based models for predicting risk factors in acute cerebral infarction patients: a clinical retrospective study

Changqing Yang et al. BMC Neurol. .

Abstract

Objectives: The aim of this study was to develop machine learning-based models for predicting acute cerebral infarction (ACI) in patients.

Methods: We extracted the data of ACI patients and non-ACI patients (as control) from two hospitals. The Lasso algorithm was employed to select the most crucial features associated with ACI. Five machine learning algorithms-based models were trained, which was performed with 10-fold cross-validation. Then, the area under the receiver operating characteristic curve (AUC), accuracy, and F1-score were calculated in the training models. Accordingly, the training models with excellent performance was selected as the final predictive model. The relative importance of variables was analyzed and ranked.

Results: A total of 150 patients were diagnosed with ACI (50.00%), with a higher proportion of males (70.67% vs. 44.00%) compared to the non-ACI patients. The logistic regression model exhibited a good performance in predicting ACI in the training set, as evidenced by its highest AUC, accuracy, sensitivity, and F1-score. Furthermore, feature importance analysis showed that blood glucose, gender, smoking history, serum homocysteine, folic acid, and C-reactive protein were the top six crucial variables of the logistic regression.

Conclusions: In our work, the ACI risk prediction model developed by the logistic regression exhibited excellent performance. This could contribute to the identification of risk variables for ACI patients and enables clinicians timely and effective interventions.

Keywords: Acute cerebral infarction; Machine learning; Prediction model; Risk factors.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
The flowchart of this study. ACI, acute cerebral infarction; SVM, support vector machine; MLP, multi-layer perceptron neural network; CNB, complement Naive Bayes; LR, logistic regression; EMR, electronic medical record. The ACI group (N = 150), the non-ACI group (N = 150)
Fig. 2
Fig. 2
Clinical features selection using the Lasso algorithm. 28 out of 36 clinical variables were finally selected to train machine learning models
Fig. 3
Fig. 3
The receiver operating characteristic curves (ROCs) of machine learning training models. AUC, area under the ROC; SVM, support vector machine; MLP, multi-layer perceptron neural network; CNB, complement Naive Bayes
Fig. 4
Fig. 4
The relative importance of the variables in the logistic regression model is in decreasing order. The relative importance of high-ranking variables in the logistic regression model is arranged in descending order (the top six): blood glucose, gender, smoking history, Hcy, folic acid, and CRP
See this image and copyright information in PMC

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