Moulaei Khadijeh, Afshari Lida, Moulaei Reza, Sabet Babak, Mousavi Seyed Mohammad, Afrash Mohammad Reza
Health Management and Economics Research Center, Health Management Research Institute, Iran University of Medical Sciences, Tehran, Iran.
Artificial Intelligence in Medical Sciences Research Center, Smart University of Medical Sciences, Tehran, Iran.
Sci Rep. 2024 Dec 28;14(1):31392. doi: 10.1038/s41598-024-82931-5.
Failure to predict stroke promptly may lead to delayed treatment, causing severe consequences like permanent neurological damage or death. Early detection using deep learning (DL) and machine learning (ML) models can enhance patient outcomes and mitigate the long-term effects of strokes. The aim of this study is to compare these models, exploring their efficacy in predicting stroke. This study analyzed a dataset comprising 663 records from patients hospitalized at Hazrat Rasool Akram Hospital in Tehran, Iran, including 401 healthy individuals and 262 stroke patients. A total of eight established ML (SVM, XGB, KNN, RF) and DL (DNN, FNN, LSTM, CNN) models were utilized to predict stroke. Techniques such as 10-fold cross-validation and hyperparameter tuning were implemented to prevent overfitting. The study also focused on interpretability through Shapley Additive Explanations (SHAP). The evaluation of model's performance was based on accuracy, specificity, sensitivity, F1-score, and ROC curve metrics. Among DL models, LSTM showed superior sensitivity at 96.15%, while FNN exhibited better specificity (96.0%), accuracy (96.0%), F1-score (95.0%), and ROC (98.0%) among DL models. For ML models, RF displayed higher sensitivity (99.9%), accuracy (99.0%), specificity (100%), F1-score (99.0%), and ROC (99.9%). Overall, RF outperformed all models, while DL models surpassed ML models in most metrics except for RF. DL models (CNN, LSTM, DNN, FNN) achieved sensitivities from 93.0 to 96.15%, specificities from 80.0 to 96.0%, accuracies from 92.0 to 96.0%, F1-scores from 87.34 to 95.0%, and ROC scores from 95.0 to 98.0%. In contrast, ML models (KNN, XGB, SVM) showed sensitivities between 29.0% and 94.0%, specificities between 89.47% and 96.0%, accuracies between 71.0% and 95.0%, F1-scores between 44.0% and 95.0%, and ROC scores between 64.0% and 95.0%. This study demonstrates the efficacy of DL and ML models in predicting stroke, with the RF models outperforming all others in key metrics. While DL models generally surpassed ML models, RF's exceptional performance highlights the potential of combining these technologies for early stroke detection, significantly improving patient outcomes by preventing severe consequences like permanent neurological damage or death.
未能及时预测中风可能导致治疗延误，从而造成永久性神经损伤或死亡等严重后果。使用深度学习（DL）和机器学习（ML）模型进行早期检测可以改善患者预后并减轻中风的长期影响。本研究的目的是比较这些模型，探索它们在预测中风方面的功效。本研究分析了一个数据集，该数据集包含来自伊朗德黑兰哈兹拉特·拉苏勒·阿克拉姆医院住院患者的663条记录，其中包括401名健康个体和262名中风患者。总共使用了八个已建立的ML（支持向量机、极端梯度提升、K近邻、随机森林）和DL（深度神经网络、前馈神经网络、长短期记忆网络、卷积神经网络）模型来预测中风。采用了10折交叉验证和超参数调整等技术来防止过拟合。该研究还通过夏普利值加法解释（SHAP）专注于可解释性。模型性能的评估基于准确率、特异性、敏感性、F1分数和ROC曲线指标。在DL模型中，长短期记忆网络显示出96.15%的卓越敏感性，而在前馈神经网络在DL模型中表现出更好的特异性（96.0%）、准确率（96.0%）、F1分数（95.0%）和ROC（98.0%）。对于ML模型，随机森林显示出更高的敏感性（99.9%）、准确率（99.0%）、特异性（100%）、F1分数（99.0%）和ROC（99.9%）。总体而言，随机森林在所有模型中表现最佳，而除随机森林外，DL模型在大多数指标上超过了ML模型。DL模型（卷积神经网络、长短期记忆网络、深度神经网络、前馈神经网络）的敏感性从93.0%到96.15%，特异性从80.0%到96.0%，准确率从92.0%到96.0%，F1分数从87.34%到95.0%，ROC分数从95.0%到98.0%。相比之下，ML模型（K近邻、极端梯度提升、支持向量机）的敏感性在29.0%至94.0%之间，特异性在89.47%至96.0%之间，准确率在71.0%至95.0%之间，F1分数在44.0%至95.0%之间，ROC分数在64.0%至95.0%之间。本研究证明了DL和ML模型在预测中风方面的功效，随机森林模型在关键指标上优于所有其他模型。虽然DL模型总体上超过了ML模型，但随机森林的卓越性能凸显了将这些技术结合用于早期中风检测的潜力，通过预防永久性神经损伤或死亡等严重后果显著改善患者预后。
