Review of Computer Engineering Research最新文献

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Machine learning algorithms-based decision support model for diabetes 基于机器学习算法的糖尿病决策支持模型

Review of Computer Engineering Research Pub Date : 2024-01-11 DOI: 10.18488/76.v11i1.3598

Karthick Kanagarathinam, R. Manikandan, T. S. Kumar

{"title":"Machine learning algorithms-based decision support model for diabetes","authors":"Karthick Kanagarathinam, R. Manikandan, T. S. Kumar","doi":"10.18488/76.v11i1.3598","DOIUrl":"https://doi.org/10.18488/76.v11i1.3598","url":null,"abstract":"This research explores the application of machine learning (ML)-based risk prediction models in early diabetes disease detection for healthcare professionals. Diabetes affects millions of people worldwide. In light of significant advancements in biomedical sciences, vast volumes of data have been generated, including high-throughput genetic and diagnostic data sourced from extensive health records. Leveraging an initial diabetes risk prediction dataset from the University of California Irvine (UCI) ML repository, our research focused on supervised learning techniques, constituting 85% of the employed methods. The remaining 15% comprised unsupervised learning approaches, specifically association rules. A key contribution of this study lies in the development of an optimal prediction model utilizing supervised ML algorithms. The Boruta feature selection algorithm was employed to identify pertinent features, and the subsequent models were validated using a preprocessed dataset containing 10 attributes. Notably, the risk prediction models generated through random forest, extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) exhibited impressive average accuracies of 98.13%, 97.37%, and 97.22%, respectively, as determined via 10-fold cross-validation with 15 repetitions. Furthermore, these models achieved exceptional area under the ROC curve (AUC) values of 1, 0.99, and 0.99, respectively, showcasing their robustness and efficacy in diabetes risk prediction.","PeriodicalId":507768,"journal":{"name":"Review of Computer Engineering Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139534230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Software reliability prediction using ensemble learning with random hyperparameter optimization 利用随机超参数优化的集合学习进行软件可靠性预测

Review of Computer Engineering Research Pub Date : 2024-01-10 DOI: 10.18488/76.v11i1.3597

G. Habtemariam, Sudhir Kumar Mohapatra, H. Seid

{"title":"Software reliability prediction using ensemble learning with random hyperparameter optimization","authors":"G. Habtemariam, Sudhir Kumar Mohapatra, H. Seid","doi":"10.18488/76.v11i1.3597","DOIUrl":"https://doi.org/10.18488/76.v11i1.3597","url":null,"abstract":"The paper investigates software reliability prediction by using ensemble learning with random hyperparameter optimization. Software reliability is a significant problem with software quality that developers face. It involves accurately predicting the next failure. In recent years, machine learning techniques and ensemble learning approaches have been applied to improve software reliability prediction. These approaches aim to analyze historical data and develop models that can accurately forecast when failures are likely to occur. The article proposes an ensemble learning regression model using Ridge, Bayesian Ridge, Support Vector Regressor (SVR), K-Nearest Neighbors Algorithm (KNN), Regression tree, Random Forest, Neural network, and Decision Tree as base learners. Ridge is used as a combiner model. Each base learner hyperparameter is tuned using a random search algorithm automatically. A random hyperparameter search optimization algorithm selects the hyperparameter and adjusts it for overfitting and underfitting. The base models are tuned to minimize bias and variance. The performances of the models are evaluated using standard error measures such as Mean Squared Error (MSE), Sum of Squared Error (SSE), and Normalized Root Mean Square Error (NRMSE). The proposed ensemble model is compared with existing models using a benchmark dataset. The Iyer,and Lee, and Musa datasets are used for the experiment. The dataset is scaled using standard methods like logarithmic scaling, lagging, and linear interpolation. The results of the statistical comparison show better performance by our proposed model as compared to existing models.","PeriodicalId":507768,"journal":{"name":"Review of Computer Engineering Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139534770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pneumonia and tuberculosis detection with chest x-ray images and medical records using deep learning techniques 利用深度学习技术通过胸部 X 光图像和病历检测肺炎和肺结核

Review of Computer Engineering Research Pub Date : 2023-11-28 DOI: 10.18488/76.v10i4.3533

Sudhir Kumar Mohapatra, Mesfin Abebe, Lidia Mekuanint, Srinivas Prasad, Prasanta Kumar Bala, Sunil Kumar Dhala

{"title":"Pneumonia and tuberculosis detection with chest x-ray images and medical records using deep learning techniques","authors":"Sudhir Kumar Mohapatra, Mesfin Abebe, Lidia Mekuanint, Srinivas Prasad, Prasanta Kumar Bala, Sunil Kumar Dhala","doi":"10.18488/76.v10i4.3533","DOIUrl":"https://doi.org/10.18488/76.v10i4.3533","url":null,"abstract":"Pneumonia and tuberculosis are the major public health problems worldwide. These diseases affect the lungs, and if they are not diagnosed properly in time, they can become a fatal health problem. Chest x-ray images are widely used to detect and diagnose Pneumonia and Tuberculosis disease. Detection of Pneumonia and Tuberculosis from chest x-ray images is difficult and requires experience due to the similar pathological features of the diseases. Sometimes a misdiagnosis of the disease occurs due to this similarity. Several researchers used deep learning and machine learning techniques to solve this misdiagnosis problem. However, these studies used the chest x-ray images only to develop Pneumonia and Tuberculosis disease detection models. But using the chest x-ray images alone cannot necessarily lead to accurate disease detection and classification. In the traditional or manual approach, medical records are required to support and correctly interpret the chest x-ray images in the appropriate clinical context. This study develops a multi-input Pneumonia and Tuberculosis detection model using chest x-ray images and medical records to follow the clinical procedure. The study applied a Convolutional Neural Network for the chest x-ray image data and a Multilayer perceptron for the medical record data to develop the models. We implemented feature-level concatenation to join the output feature vectors from the Convolutional Neural Network and a Multilayer perceptron for the development of the disease detection model. For the purpose of comparison, we also developed image-only and medical record-only models. Consequently, the image-only model gives an accuracy of 92.68%, the medical record-only model results in 98.72% accuracy, and the combined model accuracy is improved to 99.61%. In general, the study shows that the fusion of the chest x-ray and the medical records leads to better accuracy and is more similar to the clinical approach.","PeriodicalId":507768,"journal":{"name":"Review of Computer Engineering Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139227160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0