Computer methods and programs in biomedicine update最新文献

{"title":"Awareness Level of Huntington Disease: Comprehensive Analysis of Tweets During Huntington Disease Awareness Month","authors":"Nawal H Alharthi ,&nbsp;Eman M Alanazi ,&nbsp;Xiaoyu Liu","doi":"10.1016/j.cmpbup.2023.100117","DOIUrl":"10.1016/j.cmpbup.2023.100117","url":null,"abstract":"<div><h3>Background</h3><p>Unawareness of Huntington disease is prevalent where patients might have a denial of illness, less reporting of symptoms such as changes in behavior or cognitive impairment, or poor coping with the disease. Understanding the awareness level of Huntington disease is crucial to provide more suggestions for public health campaigns.</p></div><div><h3>Objective</h3><p>This study explores the level of awareness of Huntington's disease among users of social media. We will also explore the tweeting behavior during Huntington disease awareness month, and search any missing area related to the awareness by following the framework of Social Media-Based Public Health Campaigns.</p></div><div><h3>Method</h3><p>We extracted tweets from April 2021-Jun 2021. We used both quantitative and qualitative methods to analyze the data. We used Python programming and various natural language processing tools to process and analyze data for a quantitative investigation. We also carried out a qualitative content analysis to identify themes and subthemes in the data.</p></div><div><h3>Result</h3><p>We discovered that the most popular hashtag is #LetsTalkAboutHD, and after looking over the data, it seemed to us that the word \"support\" was used more than 54 times during that time. According to the findings of our analysis of the twitter distribution pattern in terms of time, the most tweets were sent between May 13 and May 16, particularly on Wednesday, which was the busiest day. Also, the United States and Alaska had the highest levels of engagement when the pattern of tweets based on geographic location was examined. The most common pattern in the tweets that we separated based on patterns was news, which was followed by research and clinical trials.</p></div><div><h3>Conclusion</h3><p>Awareness campaigns needs to follow the framework of social media-Based Public Health Campaigns to provide more comprehensive information about Huntington disease and increase the awareness level among patients and families.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"4 ","pages":"Article 100117"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46317886","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}

{"title":"How do current digital patient decision aids in maternity care align with the health literacy skills and needs of clients?: a think aloud study","authors":"Laxsini Murugesu ,&nbsp;Mirjam P. Fransen ,&nbsp;Anna L. Rietveld ,&nbsp;Danielle R.M. Timmermans ,&nbsp;Ellen M.A. Smets ,&nbsp;Olga C. Damman","doi":"10.1016/j.cmpbup.2023.100120","DOIUrl":"https://doi.org/10.1016/j.cmpbup.2023.100120","url":null,"abstract":"<div><h3>Background</h3><p>Patient decision aids (PDAs) have shown to be effective in facilitating shared decision-making (SDM) in maternity care. However, many PDAs are difficult to use for clients because of high cognitive demand.</p></div><div><h3>Objective</h3><p>This study aimed to explore how current digital PDAs support clients’ health literacy skills (understanding, appraising, and applying information) and fit their needs for support in SDM in maternity care.</p></div><div><h3>Methods</h3><p>Clients (n=21) in Dutch maternity care were invited to use five PDAs during think aloud interviews. The interviews were transcribed verbatim, coded with open and axial coding, and analysed using thematic analysis. A framework of health literacy skills for SDM was used to categorize the themes.</p></div><div><h3>Results</h3><p>Clients reported a need for support to appraise and understand the purpose of PDAs. Most clients adequately used both benefit/harm information about available options and available Value Clarification Methods (VCM), indicating that these main PDA elements supported them to actively process this information in their decision-making process. However, these elements were only appreciated and adequately used when clients understood the pregnancy- and labour related terminology used. A lack of balanced probability information about outcomes of options for mother and child hindered further information use. VCM were only used when presented attributes were relevant for clients.</p></div><div><h3>Conclusions</h3><p>Clients were in general able to process and use information presented in PDAs in maternity care tested in this study, thus PDAs were aligned with health literacy skills. Adequate understanding of terminology and perceived relevance of specific information elements were important preconditions.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"4 ","pages":"Article 100120"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49762644","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}

{"title":"Rianú: Multi-tissue tracking software for increased throughput of engineered cardiac tissue screening","authors":"Jack F. Murphy,&nbsp;Kevin D. Costa,&nbsp;Irene C. Turnbull","doi":"10.1016/j.cmpbup.2023.100107","DOIUrl":"10.1016/j.cmpbup.2023.100107","url":null,"abstract":"<div><h3>Background:</h3><p>The field of tissue engineering has provided valuable three-dimensional species-specific models of the human myocardium in the form of human Engineered Cardiac Tissues (hECTs) and similar constructs. However, hECT systems are often bottlenecked by a lack of openly available software that can collect data from multiple tissues at a time, even in multi-tissue bioreactors, which limits throughput in phenotypic and therapeutic screening applications.</p></div><div><h3>Methods:</h3><p>We developed Rianú, an open-source web application capable of simultaneously tracking multiple hECTs on flexible end-posts. This software is operating system agnostic and deployable on a remote server, accessible via a web browser with no local hardware or software requirements. The software incorporates object-tracking capabilities for multiple objects simultaneously, an algorithm for twitch tracing analysis and contractile force calculation, and a data compilation system for comparative analysis within and amongst groups. Validation tests were performed using in-silico and in-vitro experiments for comparison with established methods and interventions.</p></div><div><h3>Results:</h3><p>Rianú was able to detect the displacement of the flexible end-posts with a sub-pixel sensitivity of 0.555 px/post (minimum increment in post displacement) and a lower limit of 1.665 px/post (minimum post displacement). Compared to our established reference for contractility assessment, Rianú had a high correlation for all parameters analyzed (ranging from R<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>=0.7514 to R<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>=0.9695), demonstrating its high accuracy and reliability.</p></div><div><h3>Conclusions:</h3><p>Rianú provides simultaneous tracking of multiple hECTs, expediting the recording and analysis processes, and simplifies time-based intervention studies. It also allows data collection from different formats and has scale-up capabilities proportional to the number of tissues per field of view. These capabilities will enhance throughput of hECTs and similar assays for in-vitro analysis in disease modeling and drug screening applications.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"3 ","pages":"Article 100107"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/75/28/nihms-1909546.PMC10359020.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9848113","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}

{"title":"Comparison of the level of eHealth literacy between patients with COPD and registered nurses with interest in pulmonary diseases","authors":"Marie Knude Palshof ,&nbsp;Freja Katrine Henning Jeppesen ,&nbsp;Anne Dahlgaard Thuesen ,&nbsp;Camilla Steno Holm ,&nbsp;Eva Brøndum ,&nbsp;Lars Kayser","doi":"10.1016/j.cmpbup.2023.100121","DOIUrl":"https://doi.org/10.1016/j.cmpbup.2023.100121","url":null,"abstract":"<div><h3>Background</h3><p>This study examines the level of eHealth literacy (eHL) of COPD patients and registered nurses (RN) prior to the implementation of a new national telehealth service. The objective was to provide the nurses with an understanding of eHL and to provide knowledge about the patients’ eHL level, socio-demographic characteristics, and digital behaviour for the nurses to be better able to support the patients’ adoption and usage of telehealth.</p></div><div><h3>Method</h3><p>The eHealth Literacy Questionnaire (eHLQ) was administered in an outpatient clinic in February and March 2020 (<em>N</em> = 42). The staff-eHLQ was administered by web in November 2019 and at a conference in January 2020 (<em>N</em> = 39). The RNs were asked about workplace and experience with telehealth and the patients about gender, age, and educational level as well as their digital health behaviour.</p><p>A multiple linear regression analysis tested for relations between the socio-demographic and digital behaviour variables and the eHLQ-scores for the COPD patients.</p></div><div><h3>Results</h3><p>The RNs’ eHLQ-scores relating to engagement with information, motivation, and experience with digital services signified an insufficient eHL level which may influence their ability to motivate and promote the usage of telehealth to patients.</p><p>The patients’ scores were higher than the RNs’ with respect to motivation and experience with digital services but seemed to have an insufficient level in relation to using technology to process information and actively engage with digital services.</p></div><div><h3>Conclusion</h3><p>The patients need support in relation to processing information and interacting with services. The RNs’ eHLQ-scores being lower than the patients are problematic as it may influence how well they are able to support the adoption of the new telehealth service.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"4 ","pages":"Article 100121"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49727002","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}

{"title":"The Value of Short-term Physiological History and Contextual Data in Predicting Hypotension in the ICU Settings","authors":"Mina Chookhachizadeh Moghadam ,&nbsp;Ehsan Masoumi ,&nbsp;Samir Kendale ,&nbsp;Nader Bagherzadeh","doi":"10.1016/j.cmpbup.2023.100100","DOIUrl":"https://doi.org/10.1016/j.cmpbup.2023.100100","url":null,"abstract":"<div><p>Hypotension frequently occurs in intensive care units (ICUs) and is correlated to worsening patient outcomes. In this study, we propose a machine learning (ML) algorithm that predicts hypotensive events in ICUs by extracting the information from patients' contextual data and physiological signals. The algorithm uses patients’ history including demographics, pre-ICU medication, and pre-existing comorbidities, and only five minutes of prior physiological history to predict hypotension up to 30 min in advance. We show that adding demographic information to the physiological data does not improve the algorithm's predictive performance of 84% sensitivity, 89% positive predictive value (PPV), and 98% specificity. Furthermore, the results show that including features extracted from patients’ pre-ICU medications and comorbidities lowers the learning algorithm’ prediction performance and leads to 2% degradation in its F1-score. The feature importance analysis showed that the ratio of MAP to HR (MAP2HR) and the average of RR intervals on the ECG (RRI), both extracted from physiological signals, have the highest weights in the prediction of hypotension.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"3 ","pages":"Article 100100"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49774900","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}

{"title":"Machine learning-based diagnosis of breast cancer utilizing feature optimization technique","authors":"Khandaker Mohammad Mohi Uddin ,&nbsp;Nitish Biswas ,&nbsp;Sarreha Tasmin Rikta ,&nbsp;Samrat Kumar Dey","doi":"10.1016/j.cmpbup.2023.100098","DOIUrl":"10.1016/j.cmpbup.2023.100098","url":null,"abstract":"<div><p>Breast cancer disease is recognized as one of the leading causes of death in women worldwide after lung cancer. Breast cancer refers to a malignant neoplasm that develops from breast cells. Developed and less developed countries both are suffering from this extensive cancer. This cancer can be recuperated if it is detected at an early stage. Many researchers have proposed several machine learning techniques to predict breast cancer with the highest accuracy in the past years. In this research work, the Wisconsin Breast Cancer Dataset (WBCD) has been used as a training set from the UCI machine learning repository to compare the performance of the various machine learning techniques. Different kinds of machine learning classifiers such as support vector machine (SVM), Random Forest (RF), K-nearest neighbors(K-NN), Decision tree (DT), Naïve Bayes (NB), Logistic Regression (LR), AdaBoost (AB), Gradient Boosting (GB), Multi-layer perceptron (MLP), Nearest Cluster Classifier (NCC), and voting classifier (VC) have been used for comparing and analyzing breast cancer into benign and malignant tumors. Various matrices such as error rate, Accuracy, Precision, F1-score, and recall have been implemented to measure the model's performance. Each Algorithm's accuracy has been ascertained for finding the best suitable one. Based on the analysis, the result shows that the Voting classifier has the highest accuracy, which is 98.77%, with the lowest error rate. Finally, a web page is developed using a flask micro-framework integrating the best model using react.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"3 ","pages":"Article 100098"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43734238","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}

{"title":"Combining mathematical model for HRV mapping and machine learning to predict sudden cardiac death","authors":"Shahrzad Marjani ,&nbsp;Mohammad Karimi Moridani","doi":"10.1016/j.cmpbup.2023.100112","DOIUrl":"10.1016/j.cmpbup.2023.100112","url":null,"abstract":"<div><p>Sudden cardiac death, a prominent cause of mortality, often occurs within a narrow time window of less than an hour. This study introduces a novel methodology with the aim of early prediction of sudden cardiac death. The proposed approach involves the extraction of diverse features from the ECG signal, including the calculation of angles between two vectors, the computation of triangle areas formed by consecutive points, the determination of the shortest distance to a 450 line, and their combinations. Additionally, a thresholding technique is proposed to identify the risk period and predict the occurrence of sudden death. To assess the performance of the algorithm, data from the MIT-BH Holter database were utilized. The results obtained demonstrate that the angle feature achieves an average sensitivity of 93.75% with five false alarms, the area feature achieves an average sensitivity of 88.75% with nine false alarms, the shortest distance feature achieves an average sensitivity of 86.25% with 12 false alarms, and the combined feature achieves an average sensitivity of 96.25% with three false alarms. Remarkably, unlike existing methodologies in the literature, this method exhibits high accuracy in predicting the development of the risk of sudden cardiac death (SCD) even up to 30 min prior to onset. As a consequence, it plays a critical role in diagnosing patients' conditions and facilitating timely interventions. Moreover, the results confirm the feasibility of predicting cardiac arrest solely based on geometric features derived from variations in heart rate variability (HRV) dynamics.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"4 ","pages":"Article 100112"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41463775","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}

{"title":"Wearable devices for anxiety & depression: A scoping review","authors":"Arfan Ahmed ,&nbsp;Sarah Aziz ,&nbsp;Mahmood Alzubaidi ,&nbsp;Jens Schneider ,&nbsp;Sara Irshaidat ,&nbsp;Hashem Abu Serhan ,&nbsp;Alaa A Abd-alrazaq ,&nbsp;Barry Solaiman ,&nbsp;Mowafa Househ","doi":"10.1016/j.cmpbup.2023.100095","DOIUrl":"10.1016/j.cmpbup.2023.100095","url":null,"abstract":"<div><h3>Background</h3><p>The rates of mental health disorders such as anxiety and depression are at an all-time high especially since the onset of COVID-19, and the need for readily available digital health care solutions has never been greater. Wearable devices have increasingly incorporated sensors that were previously reserved for hospital settings. The availability of wearable device features that address anxiety and depression is still in its infancy, but consumers will soon have the potential to self-monitor moods and behaviors using everyday commercially-available devices.</p></div><div><h3>Objective</h3><p>This study aims to explore the features of wearable devices that can be used for monitoring anxiety and depression.</p></div><div><h3>Methods</h3><p>Six bibliographic databases, including MEDLINE, EMBASE, PsycINFO, IEEE Xplore, ACM Digital Library, and Google Scholar were used as search engines for this review. Two independent reviewers performed study selection and data extraction, while two other reviewers justified the cross-checking of extracted data. A narrative approach for synthesizing the data was utilized.</p></div><div><h3>Results</h3><p>From 2408 initial results, 58 studies were assessed and highlighted according to our inclusion criteria. Wrist-worn devices were identified in the bulk of our studies (<em>n</em> = 42 or 71%). For the identification of anxiety and depression, we reported 26 methods for assessing mood, with the State-Trait Anxiety Inventory being the joint most common along with the Diagnostic and Statistical Manual of Mental Disorders (<em>n</em> = 8 or 14%). Finally, <em>n</em> = 26 or 46% of studies highlighted the smartphone as a wearable device host device.</p></div><div><h3>Conclusion</h3><p>The emergence of affordable, consumer-grade biosensors offers the potential for new approaches to support mental health therapies for illnesses such as anxiety and depression. We believe that purposefully-designed wearable devices that combine the expertise of technologists and clinical experts can play a key role in self-care monitoring and diagnosis.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"3 ","pages":"Article 100095"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9884643/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9636205","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}

{"title":"Towards automatization of organoid analysis: A deep learning approach to localize and quantify organoid images","authors":"Asmaa Haja ,&nbsp;José M. Horcas-Nieto ,&nbsp;Barbara M. Bakker ,&nbsp;Lambert Schomaker","doi":"10.1016/j.cmpbup.2023.100101","DOIUrl":"10.1016/j.cmpbup.2023.100101","url":null,"abstract":"<div><p>The interest in the use of organoids in the biomedical field has increased exponentially in the past years. Organoids, or three-dimensional “mini-organs”, have the ability to proliferate and self-organize <em>in-vitro</em>, while displaying varying morphologies. When in culture, these structures can overlap with each other making the quantification and morphological characterization a challenging task. Quick and reliable analysis of organoid images could help in precisely modeling disease phenotypes as well as provide information on organ development. Therefore, automatization of the quantification and measurements is an important step towards an easier, faster, and less biased workflow.</p><p>In order to accomplish this, a free e-Science service (OrganelX) has been developed for localization and quantification of organoid size based on deep learning methods. The ability of the system was tested on murine liver organoids, and the data are made publicly available. The OrganelX e-Science free service is available at <span>https://organelx.hpc.rug.nl/organoid/</span><svg><path></path></svg>.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"3 ","pages":"Article 100101"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42429858","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}

{"title":"Improving SVM performance for type II diabetes prediction with an improved non-linear kernel: Insights from the PIMA dataset","authors":"Md.Shamim Reza ,&nbsp;Umme Hafsha ,&nbsp;Ruhul Amin ,&nbsp;Rubia Yasmin ,&nbsp;Sabba Ruhi","doi":"10.1016/j.cmpbup.2023.100118","DOIUrl":"10.1016/j.cmpbup.2023.100118","url":null,"abstract":"<div><p>Type 2 diabetes is a chronic metabolic disease that affects a significant portion of the worldwide people. Prediction of this disease using different machine learning (ML) based algorithms has gained substantial attention due to its potential for early detection and effective intervention. One of the most powerful ML algorithm support vector machines (SVM) has proven to be effective in a variety of classification tasks, including diabetes prediction. However, the kernel function chosen has a substantial effect on the performance of SVM classifiers. This paper proposes an improved non-linear kernel for the SVM model to enhance Type 2 diabetes classification. The new kernel uses radial basis function (RBF) and RBF city block kernels that enable SVM to learn complex decision boundaries and adapt to the intricacies of the PIMA dataset. The PIMA dataset contains various clinical and demographic features of individuals. To address missing values and outliers, we impute them using the median, ensuring the integrity of the dataset. We tackle the class imbalance issue by leveraging a robust synthetic-based over-sampling approach.</p><p>A comparative analysis is performed against several existing kernel functions to show that the proposed approach is superior in terms of various prediction evaluation matrices. Our recommended integrated kernel model also showed improved performance (ACC = 85.5, Recall = 87.0, Precision = 83.4, F1 score = 85.2, and AUC = 85.5) when compared to other approaches in the literature. The results of this study indicate that the proposed non-linear kernel in SVM outperforms existing kernel functions for predicting Type 2 diabetes using the PIMA dataset. Furthermore, a simulation study is carried out to robustify the proposed kernel in SVM and perform well. The improved accuracy and robustness of the model suggest its potential utility in clinical settings, aiding in the early identification and management of individuals at risk for developing diabetes.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"4 ","pages":"Article 100118"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46853720","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}