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All Journal Journal of Information Technology and Computer Engineering International Journal of Informatics, Information System and Computer Engineering (INJIISCOM)
Jamtsho, Yonten
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Smartphone-Based Heart Disease Classification Using Machine Learning Techniques Jamtsho, Yonten; Wangmo, Sonam
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 5 No. 2 (2024): INJIISCOM: VOLUME 5, ISSUE 2, DECEMBER 2024
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v5i2.12504

Abstract

Patients having heart diseases are diagnosed with a severe delay at times and further diagnosis in the absence of medical personnel can be fatal if the prediction is inaccurate. Therefore, this paper proposes the use of heart disease datasets to predict heart disease using various machine learning methods (Logistic Regression, Naive Bayes, Random Forest, k-nearest Neighbor, Support Vector Machine, Decision Tree Classifier, XGBoost Classifier, Artificial Neural Network). Cleveland, Hungarian, Switzerland, Long Beach VA and Statlog (Heart) datasets were used in this study which has 11 features of 1190 instances. The dataset was split into train and test sets with a ratio of 80:20. The performance was evaluated based on the accuracy, precision, recall, and F1 score for each of the models. From the eight models, the XGBoost Classifier outperformed other models with an accuracy of 93.7%. The trained model was integrated with the Android Studio framework to create the mobile application for the classification of heart disease.
Deep Learning-Based Dzongkha Handwritten Digit Classification Jamtsho, Yonten; Yangden, Pema; Wangmo, Sonam; Dema, Nima
JITCE (Journal of Information Technology and Computer Engineering) Vol. 8 No. 1 (2024)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.8.1.1-7.2024

Abstract

In computer vision applications, pattern recognition is one of the important fields in artificial intelligence. With the advancement in deep learning technology, many machine learning algorithms were developed to tackle the problem of pattern recognition. The purpose of conducting the research is to create the first-ever Dzongkha handwritten digit dataset and develop a model to classify the digit. In the study, the 3 layer set of CONV → ReLU → POOL, followed by a fully connected layer, dropout layer, and softmax function were used to train the digit. In the dataset, each class (0-9) contains 1500 images which are split into train, validation, and test sets: 70:20:10. The model was trained on three different image dimensions: 28 by 28, 32 by 32, and 64 by 64. Compared to image dimensions 28 by 28 and 32 by 32, 64 by 64 gave the highest train, validation, and test accuracy of 98.66%, 98.9%, and 99.13% respectively. In the future, the sample of digits needs to be increased and use the transfer learning concept to train the model.
Co-Authors Dema, Nima Wangmo, Sonam Yangden, Pema