KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Settings

Author: search.filters.author.Phankokkruad, ManopSubject: search.filters.subject.Machine Learning

Search Results

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    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.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    COVID-19 Pneumonia detection in chest X-ray images using transfer learning of convolutional neural networks
    (2020-07-24)
    Phankokkruad, Manop
    The COVID-19 pandemic is the defining global health crisis and the greatest challenge. Since its emergence and has spread rising daily worldwide. The early diagnosis of COVID-19 can improve a chance of survival and can support clinical treatment. An automatically COVID-19 pneumonia detection will support the medical diagnosis to examine the chest X-ray image. For this reason, this work intent to develop the CNN model by the process of transfer learning. The models will be created from the three pre-trained models include Xception, VGG16, and Inception-Resnet-V2 model. Then, these newly proposed models will be applied to detect COVID-19 pneumonia from the X-ray image dataset. The proposed models enhance to diagnose the chest X-ray images of patients who have pneumonia by COVID-19. Since the existing COVID-19 X-ray dataset has a small number of images containing 323 images, this work uses the data augmentation techniques to increase the virtual number of images. The results reveal that the proposed models have performed the classification task for detecting pneumonia. The proposed model achieved an accuracy level of Xception, VGG16, and Inception-Resnet-V2 is 97.19%, 95.42% and 93.87%, respectively. It reveals that the CNN model with the Xception has higher accuracy than VGG16 and Inception-ResNet-V2 model.