ArticleJournal of healthcare engineering2022
Tuberculosis Disease Diagnosis Based on an Optimized Machine Learning Model.
Article in Journal of healthcare engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of the Accuracy of Machine Learning Models for Diagnosing Pulmonary Tuberculosis: Implications for Nursing Practice and Implementation.Nursing & health sciences · 2025Pooled it
- Article
- Early Tuberculosis Detection via Privacy-Preserving, Adaptive-Weighted Deep Models.Diagnostics (Basel, Switzerland) · 2026Article
- Development of a machine learning model for early pulmonary tuberculosis diagnosis using blood test biomarkers.BMC infectious diseases · 2025Article
- Machine Learning-Based Non-Invasive Prediction of Metabolic Dysfunction-Associated Steatohepatitis in Obese Patients: A Retrospective Study.Diagnostics (Basel, Switzerland) · 2025Article
- Advanced Machine Learning for Comparative Synovial Fluid Analysis in Osteoarthritis and Rheumatoid Arthritis.Metabolites · 2025Article
- Tuberculosis diagnosis using artificial intelligence: current trends and future prospects.Frontiers in medicine · 2025Review
- A Framework for Two-class Classification of Pulmonary Tuberculosis using Artificial IntelligenceCurrent medical imaging · 2025Article
- Impact of diabetes mellitus on tuberculosis prevention, diagnosis, and treatment from an immunologic perspective.Exploration (Beijing, China) · 2024Review
- Diagnosis of pulmonary tuberculosis with 3D neural network based on multi-scale attention mechanism.Medical & biological engineering & computing · 2024Article
- Machine Learning for Prediction of Tuberculosis Detection: Case Study of Trained African Giant Pouched Rats.Online journal of public health informatics · 2024Article
- Push forward LC-MS-based therapeutic drug monitoring and pharmacometabolomics for anti-tuberculosis precision dosing and comprehensive clinical management.Journal of pharmaceutical analysis · 2024Review
- From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning.Military Medical Research · 2023Review
- Multi-Techniques for Analyzing X-ray Images for Early Detection and Differentiation of Pneumonia and Tuberculosis Based on Hybrid Features.Diagnostics (Basel, Switzerland) · 2023Article
- Multicenter Testing of a Simple Molecular Diagnostic System for the Diagnosis of Mycobacterium Tuberculosis.Biosensors · 2023Article
- Supervised machine learning algorithms to predict the duration and risk of long-term hospitalization in HIV-infected individuals: a retrospective study.Frontiers in public health · 2023Article
- Privacy Preserved Cervical Cancer Detection Using Convolutional Neural Networks Applied to Pap Smear Images.Computational and mathematical methods in medicine · 2023Article
- Olive Disease Classification Based on Vision Transformer and CNN Models.Computational intelligence and neuroscience · 2022Article
- TubIAgnosis: A machine learning-based web application for active tuberculosis diagnosis using complete blood count data.Digital healthArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computer science plays an important role in modern dynamic health systems. Given the collaborative nature of the diagnostic process, computer technology provides important services to healthcare professionals and organizations, as well as to patients and their families, researchers, and decision-makers. Thus, any innovations that improve the diagnostic process while maintaining quality and safety are crucial to the development of the healthcare field. Many diseases can be tentatively diagnosed during their initial stages. In this study, all developed techniques were applied to tuberculosis (TB). Thus, we propose an optimized machine learning-based model that extracts optimal texture features from TB-related images and selects the hyper-parameters of the classifiers. Increasing the accuracy rate and minimizing the number of characteristics extracted are our goals. In other words, this is a multitask optimization issue. A genetic algorithm (GA) is used to choose the best features, which are then fed into a support vector machine (SVM) classifier. Using the ImageCLEF 2020 data set, we conducted experiments using the proposed approach and achieved significantly higher accuracy and better outcomes in comparison with the state-of-the-art works. The obtained experimental results highlight the efficiency of modified SVM classifier compared with other standard ones.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.