The outcome in patients with brain stroke: A deep learning neural network modeling.

BACKGROUND: The artificial intelligence field is obtaining ever-increasing interests for enhancing the accuracy of diagnosis and the quality of patient care. Deep learning neural network (DLNN) approach was considered in patients with brain stroke (BS) to predict and classify the outcome by the risk factors. MATERIALS AND METHODS: A total of 332 patients with BS (mean age: 77.4 [standard deviation: 10.4] years, 50.6% - male) from Imam Khomeini Hospital, Ardabil, Iran, during 2008-2018 participated in this prospective study. Data were gathered from the available documents of the BS registry. Furthermore, the diagnosis of BS was considered based on computerized tomography scans and magnetic resonance imaging. The DLNN strategy was applied to predict the effects of the main risk factors on mortality. The quality of the model was measured by diagnostic indices. RESULTS: The finding of this study for 81 selected models demonstrated that ranges of accuracy, sensitivity, and specificity are 90.5%-99.7%, 83.8%-100%, and 89.8%-99.5%, respectively. Based on the optimal model (tangent hyperbolic activation function with the minimum-maximum hidden units of 10-20, max epochs of 400, momentum of 0.5, and learning rate of 0.1), the most important predictors for BS mortality were time interval after 10 years (accuracy = 92.2%), age category (75.6%), the history of hyperlipoproteinemia (66.9%), and education level (66.9%). The other independent variables are at moderate importance (66.6%) which include sex, employment status, residential place, smoking habits, history of heart disease, cerebrovascular accident type, blood pressure, diabetes, oral contraceptive pill use, and physical activity. CONCLUSION: The best means for dropping the BS load is effective BS prevention. DLNN strategy showed a surprising presentation in the prediction of BS mortality based on the main risk factors with an excellent diagnostic accuracy. Moreover, the time interval after 10 years, age, the history of hyperlipoproteinemia, and education level are the most important predictors for BS.

Predicting mortality in brain stroke patients using neural networks: outcomes analysis in a longitudinal study.

In this study, Neural Networks (NN) modelling has emerged as a promising tool for predicting outcomes in patients with Brain Stroke (BS) by identifying key risk factors. In this longitudinal study, we enrolled 332 patients form Imam hospital in Ardabil, Iran, with mean age: 77.4 (SD 10.4) years, and 50.6% were male. Diagnosis of BS was confirmed using both computerized tomography scan and magnetic resonance imaging, and risk factor and outcome data were collected from the hospital's BS registry, and by telephone follow-up over a period of 10 years, respectively. Using a multilayer perceptron NN approach, we analysed the impact of various risk factors on time to mortality and mortality from BS. A total of 100 NN classification algorithm were trained utilizing STATISTICA 13 software, and the optimal model was selected for further analysis based on their diagnostic performance. We also calculated Kaplan-Meier survival probabilities and conducted Log-rank tests. The five selected NN models exhibited impressive accuracy ranges of 81-85%. However, the optimal model stood out for its superior diagnostic indices. Mortality rate in the training and the validation data set was 7.9 (95% CI 5.7-11.0) per 1000 and 8.2 (7.1-9.6) per 1000, respectively (P = 0.925). The optimal model highlighted significant risk factors for BS mortality, including smoking, lower education, advanced age, lack of physical activity, a history of diabetes, all carrying substantial importance weights. Our study provides compelling evidence that the NN approach is highly effective in predicting mortality in patients with BS based on key risk factors, and has the potential to significantly enhance the accuracy of prediction. Moreover, our findings could inform more effective prevention strategies for BS, ultimately leading to better patient outcomes.

The moderating role of underlying predictors of survival in patients with brain stroke: a statistical modeling.

Determining subclinical Brain stroke (BS) risk factors may allow for early and more operative BS prevention measures to find the main risk factors and moderating effects of survival in patients with BS. In this prospective study, a total of 332 patients were recruited from 2004 up to 2018. Cox's proportional hazard regressions were used to analyze the predictors of survival and the moderating effect by introducing the interaction effects. The survival probability 1-, 5- and 10-year death rates were 0.254, 0.053, and 0. 023, respectively. The most important risk factors for predicting BS were age category, sex, history of blood pressure, history of diabetes, history of hyperlipoproteinemia, oral contraceptive pill, hemorrhagic cerebrovascular accident. Interestingly, the age category and education level, smoking and using oral contraceptive pill moderates the relationship between the history of cerebrovascular accident, history of heart disease, and history of blood pressure with the hazard of BS, respectively. Instead of considerable advances in the treatment of the patient with BS, effective BS prevention remains the best means for dropping the BS load regarding the related factors found in this study.