Introduction: The early arrival of patients with acute ischemic stroke to start of treatment by recombinant tissue plasminogen activator (rt-PA) within 4.5 hours after onset of stroke and its modeling by data mining methods is an important issue in care of stroke patients. In this paper, the aim was to provide methods to predict the time between symptom onset and hospital arrival in stroke patients and related factors, in addition to improve classification in minority class data, also to maintain the ability of classifying majority class data at an acceptable level. Methods: We included 676 patients with ischemic stroke who referred to hospital of Ardabil city in the northwest of Iran in 2018. A new method using a combination of machine learning algorithms and genetic algorithms has been proposed to solve this problem. The performances were evaluated with accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. Results: In this study, the stacking technique provides a better result (accuracy 99.51%, sensitivity 100%, and specificity 99.40%) among all other techniques. Conclusion: Results of this study showed that this model can be used as a valuable tool for clinical decision making.

Background: Body mass index (BMI) is a good method for measure the overweight and obesity among people. The aim of this study was to develop a machine learning method to classification of BMI for clinical application.Methods: In this study we used the dataset of 1316 people who selected randomly from all area of Ardabil city. Dataset included demographic and anthropometric data. Classification algorithms such as Random forest (RF), Gaussian Naïve Bayes (GNB), Decision Tree (DT), Support-Vector Machines (SVM), Multi-layer Perceptron (MLP), K-nearest neighbors (KNN) and Logistic Regression (LR) were used for classification of people based on BMI data. The performance of algorithms were evaluated with Precision, Recall, Mean Squared Errors (MSE) and Accuracy. All programing done in python.3.7 in Jupyter Notebook. Results: According to BMI, 603(45.8%) of all samples were normal and 713 (54.2%) were at-risk. The precision of RF, GNB, DT, SVM, MLP, KNN and LR for people at risk was 0.93, 0.86, 0.99, 0.82, 100, 0.82 and 0.99 respectively. Also, the accuracy of RF, GNB, DT, SVM, MLP, KNN and LR were 95%, 83%, 100%, 82%, 100%, 82% and 100 %. Conclusion: In compare classification algorithms results showed that, the LR , MLP and DT had the higher full accuracy than other algorithms in detection of people at-risk.

Introduction: Body mass index (BMI) is an acceptable method to measure overweight and obesity among the population. Objectives: The aim of this study was evaluating the application of machine learning algorithms for classifying body mass index for clinical purposes. Patients and Methods: In this descriptive study, we selected the dataset of 1316 people who selected randomly from all area of Ardabil city in Iran. Dataset included demographic and anthropometric data. Classification algorithms such as random forest (RF), Gaussian Naive Bayes (GNB), decision tree (DT), support vector machines (SVM), multi-layer perceptron (MLP), K-nearest neighbors (KNN) and logistic regression (LR) with 10-fold cross-validation were conducted to classify the data based on BMI. The performance of algorithms was evaluated with precision, recall, mean squared errors (MSE) and accuracy indices. All programing done by Python 3.7 in Jupyter Notebook. Results: According to the BMI, 603(45.8%) of all samples were normal and 713 (54.2%) were at-risk. The precision of RF, GNB, DT, SVM, MLP, KNN and LR for people at risk were 0.93, 0.86, 0.99, 0.82, 100, 0.82 and 0.99 respectively. Additionally, the accuracy of RF, GNB, DT, SVM, MLP, KNN and LR were 95%, 83%, 100%, 82%, 100%, 82% and 100 %. Conclusion: The comparison of the classifying algorithms showed that, the LR, MLP and DT had the higher accuracy than the other algorithms in detecting of people at-risk.