Healthcare analytics (New York, N.Y.)最新文献_第3页

An enhanced machine learning approach with stacking ensemble learner for accurate liver cancer diagnosis using feature selection and gene expression data 基于特征选择和基因表达数据的肝癌准确诊断的增强机器学习方法

Healthcare analytics (New York, N.Y.) Pub Date : 2024-12-12 DOI: 10.1016/j.health.2024.100373

Amena Mahmoud , Eiko Takaoka

{"title":"An enhanced machine learning approach with stacking ensemble learner for accurate liver cancer diagnosis using feature selection and gene expression data","authors":"Amena Mahmoud , Eiko Takaoka","doi":"10.1016/j.health.2024.100373","DOIUrl":"10.1016/j.health.2024.100373","url":null,"abstract":"<div><div>Liver cancer is a significant global health concern, necessitating accurate and timely diagnosis for effective treatment. Machine learning approaches have emerged as promising tools for improving liver cancer classification using gene expression data in recent years. This study presents an advanced machine learning approach for liver cancer diagnosis using gene expression data, combining feature selection techniques with a stacking ensemble learning model. Our method addresses the challenges of high dimensionality and complex patterns in genomic data to improve diagnostic accuracy and interpretability. We employed a feature selection process to identify the most relevant gene expressions associated with liver cancer. This approach reduced the dimensionality of the data while preserving crucial biological information. The selected features were then used to train a stacking ensemble model, which combined multiple base learners, including Multi-Layer Perceptron (MLP), Random Forest (RF) model, K-nearest neighbor (KNN) model, and Support vector machine (SVM), with a meta-learner Extreme Gradient Boosting (Xgboost) model to make final predictions. The stacking ensemble achieved an accuracy of (97%), outperforming individual machine learning algorithms and traditional diagnostic methods. Furthermore, the model demonstrated high sensitivity (96.8%) and specificity (98.1%), crucial for early detection and minimizing false positives.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"7 ","pages":"Article 100373"},"PeriodicalIF":0.0,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143171470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An integrated stacked convolutional neural network and the levy flight-based grasshopper optimization algorithm for predicting heart disease 一种集成的堆叠卷积神经网络和基于levy飞行的蚱蜢优化算法用于心脏病预测

Healthcare analytics (New York, N.Y.) Pub Date : 2024-12-07 DOI: 10.1016/j.health.2024.100374

Syed Muhammad Salman Bukhari , Muhammad Hamza Zafar , Syed Kumayl Raza Moosavi , Majad Mansoor , Filippo Sanfilippo

{"title":"An integrated stacked convolutional neural network and the levy flight-based grasshopper optimization algorithm for predicting heart disease","authors":"Syed Muhammad Salman Bukhari , Muhammad Hamza Zafar , Syed Kumayl Raza Moosavi , Majad Mansoor , Filippo Sanfilippo","doi":"10.1016/j.health.2024.100374","DOIUrl":"10.1016/j.health.2024.100374","url":null,"abstract":"<div><div>Cardiovascular disease is the leading cause of death worldwide, including critical conditions such as blood vessel blockage, heart failure, and stroke. Accurate and early prediction of heart disease remains a significant challenge due to the complexity of symptoms and the variability of contributing factors. This study proposes a novel hybrid model integrating a Stacked Convolutional Neural Network (SCNN) with the Levy Flight-based Grasshopper Optimization Algorithm (LFGOA) to address this challenge. The SCNN provides robust feature extraction, while LFGOA enhances the model by optimizing hyperparameters, improving classification accuracy, and reducing overfitting. The proposed approach is evaluated using four publicly available heart disease datasets, each representing diverse clinical and demographic features. Compared to traditional classifiers, including Regression Trees, Support Vector Machine, Logistic Regression, K-Nearest Neighbors, and standard Neural Networks, the SCNN-LFGOA consistently outperforms these methods. The results highlight that the SCNN-LFGOA achieves an average accuracy of 99%, with significant improvements in specificity, sensitivity, and F1-Score, showcasing its adaptability and robustness across datasets. This study highlights the SCNN-LFGOA's potential as a transformative tool for early and accurate heart disease prediction, contributing to improved patient outcomes and more efficient healthcare resource utilization. By combining deep learning with an advanced optimization technique, this research introduces a scalable and effective solution to a critical healthcare problem.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"7 ","pages":"Article 100374"},"PeriodicalIF":0.0,"publicationDate":"2024-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143171471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimized early fusion of handcrafted and deep learning descriptors for voice pathology detection and classification 优化了语音病理检测和分类的手工和深度学习描述符的早期融合

Healthcare analytics (New York, N.Y.) Pub Date : 2024-12-01 DOI: 10.1016/j.health.2024.100369

Roohum Jegan, R. Jayagowri

{"title":"Optimized early fusion of handcrafted and deep learning descriptors for voice pathology detection and classification","authors":"Roohum Jegan, R. Jayagowri","doi":"10.1016/j.health.2024.100369","DOIUrl":"10.1016/j.health.2024.100369","url":null,"abstract":"<div><div>This study presents an automated noninvasive voice disorder detection and classification approach using an optimized fusion of modified glottal source estimation and deep transfer learning neural network descriptors. A new set of modified descriptors based on a glottal source estimator and pre-trained Inception-ResNet-v2 convolutional neural network-based features are proposed for the speech disorder detection and classification task. The modified feature set is obtained using mel-cepstral coefficients, harmonic model, phase discrimination means, distortion deviation descriptors, conventional wavelet, and glottal source estimation features. Early descriptor-level fusion is employed in this study for performance enhancement-however, the fusion results in higher feature vector dimensionality. A nature-inspired slime mould algorithm is utilized to remove redundant and select the best discriminating features. Finally, the classification is performed using the K-nearest neighbor (KNN) classifier. The proposed algorithm was evaluated using extensive experiments with different feature combinations, with and without feature selection, and with two popular datasets: the Arabic Voice Pathology Database (AVPD) and the Saarbrucken Voice Database (SVD). We show that the proposed optimized fusion method attained an enhanced voice pathology detection accuracy of 98.46%, encompassing a wide spectrum of voice disorders on the SVD database. Furthermore, compared to traditional handcrafted and deep neural network-based techniques, the proposed method demonstrates competitive performance with fewer features.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"6 ","pages":"Article 100369"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142742998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

e-Health and artificial intelligence: Emerging trends, models, and applications 电子医疗和人工智能：新兴趋势、模型和应用

Healthcare analytics (New York, N.Y.) Pub Date : 2024-12-01 DOI: 10.1016/j.health.2024.100354

Yu-Chen Hu, Pelin Angin, Haiming Liu, Debnath Bhattacharyya

引用次数: 0

An open-source application for obtaining retrospective and prospective insights into overall hospital quality star ratings 一个开源应用程序，用于获得回顾性和前瞻性的整体医院质量星级评级

Healthcare analytics (New York, N.Y.) Pub Date : 2024-12-01 DOI: 10.1016/j.health.2024.100371

Kenneth J. Locey, Brian D. Stein, Ryan Schipfer, Brittnie Dotson, Leslie Klemp

{"title":"An open-source application for obtaining retrospective and prospective insights into overall hospital quality star ratings","authors":"Kenneth J. Locey, Brian D. Stein, Ryan Schipfer, Brittnie Dotson, Leslie Klemp","doi":"10.1016/j.health.2024.100371","DOIUrl":"10.1016/j.health.2024.100371","url":null,"abstract":"<div><div>Overall Hospital Quality Star Ratings (overall star ratings) are designed to assist healthcare consumers by summarizing dozens of hospital quality measures. These ratings are also used by hospitals to direct quality improvements and are often used in healthcare research. However, no analytical tools have been developed to provide insights into the data, measures, and scores of the overall star rating system. To this end, we developed a novel open-source application to provide retrospective insights, prospective estimates, and research-ready data. Users can 1) examine changes in hospital performance from 2021 onward, 2) recalculate overall star ratings based on hypothetical improvements, 3) download data for all hospitals included in the overall star rating system since 2021, and 4) obtain prospective estimates based on the overall star rating methodology and its data source (Care Compare). We demonstrate 99.6% accuracy when estimating overall star ratings six months prior to public release. Estimates of whether hospitals will retain their star rating are up to 90% accurate a year before public release. We discuss the use of our application in healthcare research and the potential for similar tools to be developed for other hospital rating and ranking systems.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"6 ","pages":"Article 100371"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A metafrontier and Malmquist productivity index approach for analyzing biased technological and efficiency change in Taiwanese traditional Chinese medicine 台湾中医药偏激技术与效率变迁之超前沿与Malmquist生产力指数分析

Healthcare analytics (New York, N.Y.) Pub Date : 2024-12-01 DOI: 10.1016/j.health.2024.100372

Kuan-Chen Chen , Hsiang-An Yu , Ming-Miin Yu

{"title":"A metafrontier and Malmquist productivity index approach for analyzing biased technological and efficiency change in Taiwanese traditional Chinese medicine","authors":"Kuan-Chen Chen , Hsiang-An Yu , Ming-Miin Yu","doi":"10.1016/j.health.2024.100372","DOIUrl":"10.1016/j.health.2024.100372","url":null,"abstract":"<div><div>This study assesses changes in resource productivity in traditional Chinese medicine (TCM) system across Taiwanese counties and cities from 2016 to 2019, stratifying the analysis by population densities. Employing a data envelopment analysis (DEA) metafrontier Malmquist productivity index model, this research relaxes Hicks' neutrality assumption of technical change, allowing for the measurement of biased technological change and technical gap ratio changes. The empirical findings reveal a decline in TCM system productivity, primarily attributed to reduced technological advancements. Notably, higher productivity changes were observed in counties and cities with lower population densities, contrasting with those having higher population densities, where productivity changes were limited. The results suggest that areas with lower population densities hold significant potential for technological enhancement, as evidenced by intergroup technology updates and technological leadership indices. Furthermore, the estimates of productivity change and technological bias underscore the inadequacy of assuming Hicks’ neutral technological change for analyzing TCM system productivity in Taiwan. These findings highlight the need for improved TCM system technology and innovation within the healthcare system to address the urban-rural gap effectively.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"6 ","pages":"Article 100372"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial intelligence and diagnostic healthcare using computer vision and medical imaging 使用计算机视觉和医学成像的人工智能和诊断医疗保健

Healthcare analytics (New York, N.Y.) Pub Date : 2024-12-01 DOI: 10.1016/j.health.2024.100352

Gaurav Dhiman, Wattana Viriyasitavat, Atulya K. Nagar, Oscar Castillo

引用次数: 0

Machine learning for smart health and distributed biomedical services 智能健康和分布式生物医学服务的机器学习

Healthcare analytics (New York, N.Y.) Pub Date : 2024-12-01 DOI: 10.1016/j.health.2024.100363

Chinmay Chakraborty, Saïd Mahmoudi, Guangjie Han, Rubén González Crespo

引用次数: 0

A deep neural network model with spectral correlation function for electrocardiogram classification and diagnosis of atrial fibrillation 用于心电图分类和心房颤动诊断的带频谱相关函数的深度神经网络模型

Healthcare analytics (New York, N.Y.) Pub Date : 2024-11-23 DOI: 10.1016/j.health.2024.100370

Sara Mihandoost

{"title":"A deep neural network model with spectral correlation function for electrocardiogram classification and diagnosis of atrial fibrillation","authors":"Sara Mihandoost","doi":"10.1016/j.health.2024.100370","DOIUrl":"10.1016/j.health.2024.100370","url":null,"abstract":"<div><div>Atrial Fibrillation (AF) is a common type of irregular heartbeat, and early detection can significantly improve treatment outcomes and prognoses. Single-lead Electrocardiogram (ECG) devices are under extensive scrutiny for monitoring patients' heart health worldwide. Standardized ECG signal monitoring has demonstrated a significant reduction in mortality rates associated with severe cardiovascular diseases. However, the automatic detection method for AF requires significant improvement. This study presents a novel approach that utilizes the cyclostationary analysis of ECG signals, uncovering a spectral hidden periodicity between the QRS-T (the main wave components representing electrical activity in the heart) complexes of the ECG signal through the Spectral Correlation Function (SCF). To validate the proposed method's performance, the single ECG's SCF coefficients are applied to the Convolutional Recurrent Neural Network (CRNN), which consists of convolutional and long short-term memory (LSTM) layers, on the 2017 PhysioNet challenge dataset. The obtained results demonstrate that the proposed approach efficiently represents ECG signals through SCF coefficients, leading to the accurate detection of AF with an average accuracy of 92.76% and an average F1-score of 89.1%.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"6 ","pages":"Article 100370"},"PeriodicalIF":0.0,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An ensemble convolutional neural network model for brain stroke prediction using brain computed tomography images 利用脑计算机断层扫描图像预测脑中风的集合卷积神经网络模型

Healthcare analytics (New York, N.Y.) Pub Date : 2024-10-29 DOI: 10.1016/j.health.2024.100368

Most. Jannatul Ferdous, Rifat Shahriyar

{"title":"An ensemble convolutional neural network model for brain stroke prediction using brain computed tomography images","authors":"Most. Jannatul Ferdous, Rifat Shahriyar","doi":"10.1016/j.health.2024.100368","DOIUrl":"10.1016/j.health.2024.100368","url":null,"abstract":"<div><div>A stroke is a potentially fatal brain attack that causes an interruption in the blood supply to the brain. As a result, brain cells start to die due to a lack of oxygen and nutrients. After a stroke, every minute is critical. A million or more brain cells perish every minute during a stroke. The prompt identification of a stroke can prevent lasting brain damage or even save the patient’s life. Doctors advise computed tomography (CT) images of the brain for earlier stroke detection. If doctors delay CT diagnosis or may make erroneous diagnoses, this can be life-threatening. For that reason, an automatic diagnosis of stroke from a brain CT scan image will be beneficial for stroke patients. This study moderates three pre-trained convolutional neural network (CNN) models named Inceptionv3, MobileNetv2, and Xception by updating the top layer of those models using the transfer-learning technique based on CT images of the brain. A new ensemble convolutional neural network (ENSNET) model is proposed for automatic brain stroke prediction from brain CT scan images. ENSNET is the average of two improved CNN models named InceptionV3 and Xception. We have relied on the following metrics: accuracy, precision, recall, f1-score, confusion matrix, accuracy versus epoch, loss versus epoch, and the receiver operating characteristic (ROC) curve to assess performance matrices. The accuracy of the moderated Inceptionv3 is 97.48%, the moderated MobileNetv2 is 83.29%, and the moderated Xception is 96.11%. Nonetheless, the suggested ensemble model ENSNET performs better than the other models when it comes to the diagnosis of stroke from brain CT scans, providing 98.86% accuracy, 97.71% precision, 98.46% recall, 98.08% f1-score, and 98.74% area under the ROC curve(AUC). Therefore, the proposed model ENSNET can detect strokes from computed tomography images of the brain more successfully than other models.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"6 ","pages":"Article 100368"},"PeriodicalIF":0.0,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0