Informatics in Medicine Unlocked最新文献

{"title":"Seroprevalence of hepatitis B infection among blood donors in Western zone of Tanzania","authors":"Gadiel J. Marira ,&nbsp;Esther G. Kimaro ,&nbsp;Elingarami Sauli","doi":"10.1016/j.imu.2024.101518","DOIUrl":"https://doi.org/10.1016/j.imu.2024.101518","url":null,"abstract":"<div><h3>Background</h3><p>There is limited information on burden of hepatitis B infection in the Western zone of Tanzania. In this study, we analyzed a dataset from blood donors to determine Hepatitis B virus (HBV) seroprevalence and related socio-demographic factors among blood donors in the Western regions of Tanzania.</p></div><div><h3>Material and methods</h3><p>This was a cross-sectional retrospective hospital-based study, in which data were retrieved from the blood donor dataset at the Zonal Blood Transfusion Center. The analyzed information from the dataset included reported Transfusion Transmissible Infections (TTIs), which included Hepatitis B, donor demographics, donor status, donor type, donation place, and the year of donation. The analyzed data was retrieved within five years from January 2018 to December 2022. Rates of hepatitis B surface antigen (HBsAg) were determined and univariate and multivariate analyses were conducted to determine the association between infection and demographic risk factors.</p></div><div><h3>Results</h3><p>A total of 9604 retrospective blood donors were screened. Majority 8963 (93.3 %) were men, and most of them were under 45 years (89.6 %). Overall, HBsAg seroprevalence was 6.9 % (661), with Katavi (7.8 %) being relatively higher in the studied three regions. The highest HBsAg seroprevalence of 8.2 % (199) was found in the age group ranging from 35 to 44 years. Moreover, 2 (9.5 %) polygamists and 15 (17.1 %) car drivers had relatively high seroprevalence. Results from the multivariate analysis indicated that, car drivers (OR 5.44, 95 % CI; 2.43–12.20, p &lt; 0.001), and first-time donors (OR 5.19, 95 % CI 2.56 = 10.52, P &lt; 0.001), were highly associated with increased chance of getting hepatitis B infection.</p></div><div><h3>Conclusion</h3><p>The findings from this study indicated that, there was high seroprevalence of HBV infection in the Western regions of Tanzania during the studied time period. These findings call for more advocacy on HBV immunization for all groups of persons found at high risk for HBV infection.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"48 ","pages":"Article 101518"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824000741/pdfft?md5=c03e612e78897f41dc7c5ebd90ddc9c2&pid=1-s2.0-S2352914824000741-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140900965","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":"Elucidating B4GALNT1 as potential biomarker in hepatocellular carcinoma using machine learning models and mutational dynamics explored through MD simulation","authors":"Rohit Kumar Verma ,&nbsp;Kiran Bharat Lokhande ,&nbsp;Prashant Kumar Srivastava ,&nbsp;Ashutosh Singh","doi":"10.1016/j.imu.2024.101514","DOIUrl":"https://doi.org/10.1016/j.imu.2024.101514","url":null,"abstract":"<div><p>Liver hepatocellular carcinoma (LIHC) is considered one of the primary contributors to cancer-related mortality on a global scale. The identification of new biomarkers is of utmost importance due to the fact that patients with LIHC are frequently detected at advanced stages, leading to an increased mortality rate. The study utilized TCGA-LIHC gene expression datasets to identify biomarkers and to address the complexity of datasets. A combination of feature selection (FS) techniques was used, and the performance of this strategy was assessed using ten machine learning classifiers. The findings were integrated, revealing biomarkers identified through at least five FS techniques. Through our proposed approach, we identified 55 potential biomarkers for LIHC. The Gaussian Naive Bayes Classifier (AUC = 0.99) was found to be the most effective classifier, achieving 98.67% accuracy when utilizing the 55 identified biomarkers in the test dataset. Additionally, we conducted differential gene expression, survival analysis, and enrichment analysis for all the identified biomarkers. Subsequently, Lasso-penalized Cox regression further refined the identified biomarkers to thirteen. Out of thirteen genes, we singled out B4GALNT1 because of its statistical significance in differential expression analysis and increasing importance across various cancer types, including LIHC. We carried out comprehensive bioinformatics and molecular dynamics simulation studies along with other structural analysis of B4GALNT1 in LIHC. In LIHC, six mutations (P64Q, S131F, A311S, R340Q, D478H, and P507Q) have been predicted to be probably damaging by evaluating in-silico prediction algorithms. In comparison to the wild type, the B4GALNT1 variations, specifically P64Q and S131F, demonstrate increased stability. However, these mutations lead to decreased atomic fluctuations, indicating a rigid protein structure. Again, mutations like A311S and P507Q induce increased flexibility, highlighting their structural impact on B4GALNT1. The study demonstrated the combination of various feature selection methods effectively reveals new biomarkers, thereby directly impacting their biological significance. Furthermore, our findings indicate a link between increased B4GALNT1 expression in individuals with liver cancer and a poorer prognosis, highlighting its potential as a promising therapeutic target.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"48 ","pages":"Article 101514"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824000704/pdfft?md5=fc03e97d776921a1dbf9039b163e1a45&pid=1-s2.0-S2352914824000704-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140900979","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":"Corrigendum to “Implementation of human whole genome sequencing data analysis: A containerized framework for sustained and enhanced throughput” [Inform. Med. Unlocked (2021) 100684]","authors":"","doi":"10.1016/j.imu.2024.101466","DOIUrl":"10.1016/j.imu.2024.101466","url":null,"abstract":"","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"49 ","pages":"Article 101466"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824000224/pdfft?md5=567293459d2092da0a8f3a0a64765376&pid=1-s2.0-S2352914824000224-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140086179","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":"Predictive biomarker discovery in cancer using a unique AI model based on set theory","authors":"Anthoula Lazaris ,&nbsp;Migmar Tsamchoe ,&nbsp;Susan Kaplan ,&nbsp;Peter Metrakos ,&nbsp;Nathan Hayes","doi":"10.1016/j.imu.2024.101481","DOIUrl":"https://doi.org/10.1016/j.imu.2024.101481","url":null,"abstract":"<div><p>The current study applies a new artificial intelligence (AI) method, ALiX, which is based on interval arithmetic, to analyze and interpret biological data for a clinical problem: identification of biomarkers for cancer diagnosis. The key unique and important feature of this study is that ALiX provides an explanation to our clinical hypothesis in the form of a list of ranked protein biomarkers that identifies which biomarkers are the most significant drivers of the predicted outcome, a capability that is not currently available in other AI methods. Based on the significant drivers, this study identifies a machine learning model and solution for stratifying cancer patients into subtypes that will predict response to treatment.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"46 ","pages":"Article 101481"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824000376/pdfft?md5=326258b16e753fc14ca4736843412893&pid=1-s2.0-S2352914824000376-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140195904","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":"A study on initial productivity trend in the transition of the ICD-10 to ICD-11 morbidity coding in Iran","authors":"Zahra Azadmanjir ,&nbsp;Abbas Sheikhtaheri ,&nbsp;Javad Zarei ,&nbsp;Reza Golpira ,&nbsp;Hooman Bakhshandeh ,&nbsp;Akram Vahedi ,&nbsp;Nasim Hashemi","doi":"10.1016/j.imu.2023.101440","DOIUrl":"https://doi.org/10.1016/j.imu.2023.101440","url":null,"abstract":"<div><h3>Background</h3><p>The International Classification of Diseases 11th revision (ICD-11) serves a wide extent of uses and provides detailed information on the range, causes, and effects of human disease and death through the reported and coded data.</p></div><div><h3>Objective</h3><p>Concerning the ICD-11 classification system, the present study was conducted to implement the ICD-11 and evaluate coding productivity in medical coders following a 1-month training program.</p></div><div><h3>Methods</h3><p>An observational study was conducted in two general hospitals. During the four months from August to November, twelve trained coders coded 1,909 inpatient records. The timing of medical record reading and diagnostic coding with ICD-10 and ICD-11 was documented separately in minutes as a self-report. The trend of coding productivity changes was analyzed to evaluate productivity in the first months of ICD-11 implementation.</p></div><div><h3>Results</h3><p>For record this research, 1475 medical records were included. The overall productivity loss was 42.24 % in the first three months after ICD-11 use. Productivity at the end of the fourth month was slightly better than baseline ICD-10 coding. Trauma cases required more coding time as more details should be coded for post-coordination.</p></div><div><h3>Conclusion</h3><p>Regarding the comprehensive documentation of medical records and the completeness of the details needed for coding with ICD-11 along with the instruction of the principles of ICD-11 coding rules and convention, the time required for coding can be significantly reduced when transitioning to the ICD-11 coding system. It can be hoped that after the four months of training and mentoring the coders, the coding speed will return to the baseline.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"44 ","pages":"Article 101440"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914823002861/pdfft?md5=a39ecb63d77f410943dc5f8c0a4d868d&pid=1-s2.0-S2352914823002861-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139108904","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":"Modeling the effects of vaccine efficacy and rate of vaccination on the transmission of pulmonary tuberculosis","authors":"Erick Mutwiri Kirimi ,&nbsp;Grace Gakii Muthuri ,&nbsp;Cyrus Gitonga Ngari ,&nbsp;Stephen Karanja","doi":"10.1016/j.imu.2024.101470","DOIUrl":"https://doi.org/10.1016/j.imu.2024.101470","url":null,"abstract":"<div><p>Numerous prevention intervention strategies have been developed to curtail the spread of pulmonary tuberculosis to susceptible populations. However, pulmonary tuberculosis continues to claim many lives worldwide. In this paper, a deterministic mathematical model incorporating an asymptomatic infectious population, considering vaccine efficacy, and vaccination rate, has been formulated. The model includes asymptomatic infectious individuals since they spread infections incessantly to susceptible populations without being noticed, thus contributing to the high transmission rate. Sensitivity and numerical analysis have been conducted to investigate the impact of varying vaccine efficacy and vaccination rates on the transmission of pulmonary tuberculosis infections from the asymptomatic infectious population. The sensitivity and numerical results show that an increase in vaccine efficacy reduces the asymptomatic infectious population and subsequently lowers the transmission rate of infections. Moreover, an increase in vaccine efficacy was shown to reduce the control reproduction number due to asymptomatic infectious individuals, thereby decreasing the transmission of pulmonary tuberculosis to susceptible populations. Further results indicate that an increase in vaccination rate reduces the control reproduction number due to asymptomatic infectious individuals, consequently lowering the rate of infection transmission. These findings emphasize the need to develop a vaccine of higher efficacy to reduce infection transmission to susceptible populations by the asymptomatic infectious individuals. Additionally, the results underscore the importance of increasing vaccination rates to eradicate pulmonary tuberculosis from the population.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"46 ","pages":"Article 101470"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824000261/pdfft?md5=59cfb545aaf72bd46c326b9ee9518880&pid=1-s2.0-S2352914824000261-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140181382","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":"Dose measurement of optic chiasm and parotid organs using OCTAVIUS 4D phantom: a dynamic IMRT method for nasopharyngeal cancer treatment","authors":"Laya Karimkhani ,&nbsp;Elham Saeedzadeh ,&nbsp;Dariush Sardari ,&nbsp;Seied Rabi Mahdavi","doi":"10.1016/j.imu.2024.101479","DOIUrl":"https://doi.org/10.1016/j.imu.2024.101479","url":null,"abstract":"<div><h3>Introduction</h3><p>In intensity-modulated radiation therapy (IMRT) techniques, although the dose conformity increases, the out-of-field doses would not decrease. This study aimed to assess the dose error calculated by the treatment planning system (TPS) in the out-of-field regions using the dynamic IMRT (D-IMRT) method in nasopharyngeal cancer (NPC) patients.</p></div><div><h3>Methods</h3><p>The out-of-field doses were measured for the chiasm and parotid organs using the D-IMRT technique (6 MV energy) with Monaco TPS. Computed tomography (CT) images of 10 NPC patients (54–77 years, mean: 61.6 ± 12.2 years) were considered and countered using 7-field and 11-field methods. The OCTAVIUS 4D phantom was utilized for dose assessment.</p></div><div><h3>Results</h3><p>According to the OCTAVIUS measurements, the Monaco TPS dose errors ranged from −58.8 to 105.5%. The average dose error for optic chiasm and parotid organs was −25% and 8.5%, respectively, with several cases falling within tolerance (±5%).</p></div><div><h3>Conclusion</h3><p>There were considerable dose calculation errors by Monaco TPS for organs located in out-of-field regions (optic chiasm and parotid) during IMRT for NPC patients. Therefore, accurate dose estimation in the out-of-field regions should be considered in clinical practices.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"46 ","pages":"Article 101479"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824000352/pdfft?md5=b9aec1cd6136252ad6eb04c8bd722b45&pid=1-s2.0-S2352914824000352-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140188054","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":"Unveiling HuB genes and drug design against Helicobacter pylori infection by network biology and biophysics techniques","authors":"Saba Javed ,&nbsp;Sajjad Ahmad ,&nbsp;Anam Naz ,&nbsp;Asad Ullah ,&nbsp;Salma Mohammed Aljahdali ,&nbsp;Yasir Waheed ,&nbsp;Alhanouf I. Al-Harbi ,&nbsp;Syed Ainul Abideen ,&nbsp;Adnan Rehman ,&nbsp;Muhammad Khurram","doi":"10.1016/j.imu.2024.101468","DOIUrl":"https://doi.org/10.1016/j.imu.2024.101468","url":null,"abstract":"<div><p><em>Helicobacter pylori (H</em>. <em>pylori)</em> is mainly considered for causing chronic gastritis, which can lead to several secondary complications like peptic ulcer and pre-malignant lesions for example atrophic gastritis, intestinal dysplasia and metaplasia, with the etiological factor of developing gastric cancer. Recent research demonstrates that <em>H</em>.<em>pylori</em> colonizes the stomach mucosa of more than fifty populations around the globe. This research focuses on unveiling hub genes, and diagnostic and drug targets against said organism by utilizing various types of networking biology and biophysical approaches. In data retrieval, the GSE19826 dataset was obtained from the gene expression omnibus database and microarray data set from array express. Geo2r analysis predicted a total number of 7 DEGs and 10 hub genes, next functional protein association network analysis (STRING) unveiled that among 10 Hub genes only 3 genes were found more interactive with other genes and involved in pathogenesis, The shortlisted three genes were further analyzed for survival analysis using Gene Expression Profiling Interactive Analysis (GEPIA) and predicted the survival rate of targeted genes. Moreover, functional enchainment analysis was done using the ToppFun server, the server predicted that COL11A1 and COL10A1 were more involved in the pathogenesis of the <em>H</em>. <em>pylori</em> infection. Furthermore, the COL10A1 gene was subjected to protein structure prediction. In molecular docking analysis, the asinex antibacterial library was screened for potential inhibitors, and one compound was predicted as a strong inhibitor with the best binding at −10.23 kcal/mol. The docking results were further validated through molecular dynamic simulation analysis and the MD simulation analysis evaluated the dynamic movement of the docked complex in various nanoseconds, the MD simulation results predicted that the docked complexes are stable throughout the simulation and can be used as a potential inhibitor against the said pathogen, however experimental study is required to further validate the predicted results and design drug against targeted pathogen.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"46 ","pages":"Article 101468"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824000248/pdfft?md5=6287522c888c99928429fdcbd317a1f2&pid=1-s2.0-S2352914824000248-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140123333","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":"Exploring potential pathways and biomarkers of pancreatic cancer associated with lynch syndrome and type 2 diabetes: An integrated bioinformatics analysis","authors":"Md. Arif Hossen ,&nbsp;Md Tanvir Yeasin ,&nbsp;Md. Arju Hossain ,&nbsp;Umme Mim Sad Jahan ,&nbsp;Moshiur Rahman ,&nbsp;Anik Hasan Suvo ,&nbsp;Md Sohel ,&nbsp;Mahmuda Akther Moli ,&nbsp;Md. Khairul Islam ,&nbsp;Mohammad Nasir Uddin ,&nbsp;Md Habibur Rahman","doi":"10.1016/j.imu.2024.101527","DOIUrl":"10.1016/j.imu.2024.101527","url":null,"abstract":"<div><p>Pancreatic cancer (PC) is a devastating malignancy with intricate genetic underpinnings and a complex etiology. Emerging evidence suggests the presence of lynch syndrome (LS) and type 2 diabetes (T2D) associated susceptibility to PC. This study presents integrated computational and systems biology approaches to identify the genetic risk factors underlying the association between PC, LS, and T2D. Patient data for these three diseases have been collected from NCBI and differentially expressed genes (DEGs) identified by the GREIN web platform. Furthermore, protein-protein interaction (PPI), gene ontology (GO), and signaling pathway networks were analyzed through STRING and DAVID databases, respectively. Autodock Vina has been used for prospective analysis of ligand-protein interaction. About 60 unique common DEGs were identified by statistical analysis. In addition to the utilization of five distinct algorithms within the Cytoscape framework, we have reported three potential target candidates: TNF, CXCL1, and TNFSF10. In particular, the immune and inflammatory response, the chemokine-mediated signaling pathway, rheumatoid arthritis, and IL-17 signaling pathways emerged as prominently enriched pathways. Furthermore, the interaction of 162 phytochemicals from <em>Nigella sativa was assessed</em> with the identified hub proteins. Among these, thujopsene emerged as a notable ligand candidate, demonstrating the most favorable binding energy against the TNF (−9.6 kca/mol TNFSF10 (−8.5 kcal/mol), and CXCL1 (−9.1 kcal/mol) proteins. Besides, pharmacokinetics, toxicity, and drug-likeness properties of the thujopsene ligand showed an acceptable range for selection of a drug candidate. Collectively, these findings shed light on the intricate interplay of genes, pathways, and potential therapeutic compounds, providing a basis for further exploration and validation in the context of relevant diseases.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"48 ","pages":"Article 101527"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824000832/pdfft?md5=b63b1568631a4ca24f3435df28599d8f&pid=1-s2.0-S2352914824000832-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141280905","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":"Empowering early detection: A web-based machine learning approach for PCOS prediction","authors":"Md Mahbubur Rahman ,&nbsp;Ashikul Islam ,&nbsp;Forhadul Islam ,&nbsp;Mashruba Zaman ,&nbsp;Md Rafiul Islam ,&nbsp;Md Shahriar Alam Sakib ,&nbsp;Hafiz Md Hasan Babu","doi":"10.1016/j.imu.2024.101500","DOIUrl":"https://doi.org/10.1016/j.imu.2024.101500","url":null,"abstract":"<div><p>Nowadays, Polycystic Ovary Syndrome (PCOS) affects many women, making it a prevalent concern. It is a hormonal disorder that causes irregular, delayed, or absent menstrual cycles in the female body. This condition can lead to the development of type 2 diabetes, gestational diabetes, weight gain, unwanted body hair, and various other complications. In severe cases, PCOS can result in infertility, posing a challenge for patients trying to conceive. Statistics show that the incidence rate of PCOS has significantly increased in recent years, which is alarming. If PCOS is identified early, people may follow their doctor's recommendations and live a better life. The dataset used for this research contains records for 541 patients. The aim of this study is to employ machine learning models to identify patterns in this disorder. The information learned is then inputted into various algorithms to assess accuracy, specificity, sensitivity, and precision using different ML models, such as Logistic Regression (<span>LR</span>), Decision Tree (DT), AdaBoost (AB), Random Forest (RF), and Support Vector Machine (SVM) among others. The research utilized the Mutual Information model for feature selection and compared the models to determine the most accurate one. Employing the Mutual Information model for feature engineering, AB and RF achieved the highest accuracy of 94 %.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"47 ","pages":"Article 101500"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S235291482400056X/pdfft?md5=2cef12dfa1fc5dd1ce2945394abebbc8&pid=1-s2.0-S235291482400056X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140807087","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}