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    Item type:Publication,
    Performance Analysis and Comparison of Cerebral Stroke Prediction Models on Imbalanced Datasets
    (2022-01-01)
    Phankokkruad, Manop
    ;
    Wacharawichanant, Sirirat
    A cerebral stroke is an interrupt blood flow to the brain leading cause of death. A number of risk factors increase the risk of stroke occurence because of lifestyle. Machine learning is effective techniques can be applied in prediction of stroke. The different kind of algorithms give the various accuracy and performance in the prediction. This study has proposed the four machine learning algorithm for classifiers to predict of cerebral stroke. The proposed model with various classifier has considered the risk factors such as age, hypertension, heart disease, average glucose level, BMI, and smoking status as feature attributes to predict cerebral stroke. This study conducted on two stroke datasets, and improve the imbalanced of between classes by using SMOTE. The result shows that XGBoost provided the highest accuracy of around 98.08% and 96.73% by comparing to the other machine learning algorithms. In addition, this study evaluates the models by analyzing the statistical parameters include accuracy, precision, sensitivity, F1 score, and AUC. The evaluation reveals that the XGBoost, Random Forest, AdaBoost and KNN classifier achieved the average AUC value of 0.851, 0.868, 0.670 and 0.851, respectively. All models provided the high confidence values, whereas the model with XGBoost classifier gave the highest performance.
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    Runner BIB number recognition system
    (2017-12-13)
    Anuntachai, Anuntapat
    ;
    Chaorattana, Wanatphong
    ;
    Boonchoay, Jutatip
    This research represents the runner BIB number recognition system to develop image processing study which solves problems and increases efficiency about runner image management in running fairs. The runner BIB number recognition system processes runner image to recognize BIB number and time when runner appears in media. The information from processing has collected to applicative later. BIB number position is on BIB tag which attach on runner body. To recognize BIB number, the system detects runner position first. This process emphasize on runner face detection in images following to concept of researcher then find BIB number in body-thigh area of runner. The system recognizes BIB number from BIB tag which represents in media. This processing presents 0.80 in precision value, 0.81 in recall value and F-measure is 0.80. The results display the runner BIB number recognition system has developed with high efficiency and can be applied for runner online communities in actual situation. The runner BIB number recognition system decreases problems about runner image processing and increases comfortable for runners when find images from running fairs. Moreover, the system can be applied in commercial to increase benefits in running business.
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    Item type:Publication,
    A new ensemble model based on linear mapping, nonlinear mapping, and probability theory for classification problems
    (2015-08-24)
    Charleonnan, Anusorn
    ;
    Jaiyen, Saichon
    Currently, various perspectives of neural networks are proposed for solving classification problems. Some of them are based on two types of mapping functions, namely, linear and nonlinear, for mapping an input space into a feature space. In addition, some neural networks are proposed based on probability theory. Since some models are appropriated for some kinds of data, depending on a distribution of the data, some data are appropriated for linear mapping, some is for nonlinear mapping, and some is for probabilistic models. Due to the fact that the data distribution in classification problems are various, we propose the new ensemble model based on linear mapping, nonlinear mapping, and probability theory for classification problems. According to the experimental results, they have shown that our proposed model can improve the accuracy of classification on the testing data sets.