ArticleScientific reports2021
Enhancing the weighted voting ensemble algorithm for tuberculosis predictive diagnosis.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed, 91 citations in OpenAlex.
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- GBWOEM: A Gradient-Based Weight Optimization Model for Improved Predictive Accuracy in Healthcare.F1000Research · 2025Article
- Construction of an intelligent screening model for allergic rhinitis based on routine blood tests.PloS one · 2025Article
- Ensemble Algorithm for Risk Prediction of Clinical Failure After Anterior Cruciate Ligament Reconstruction.Orthopaedic journal of sports medicine · 2024Article
- Non-invasive biomarkers for detecting progression toward hypovolemic cardiovascular instability in a lower body negative pressure model.Scientific reports · 2024Article
- Mapping Potential Malaria Vector Larval Habitats for Larval Source Management in Western Kenya: Introduction to Multimodel Ensembling Approaches.The American journal of tropical medicine and hygiene · 2024Article
- YOLOv8's advancements in tuberculosis identification from chest images.Frontiers in big data · 2024Article
- An ensemble-based machine learning solution for imbalanced multiclass dataset during lithology log generation.Scientific reports · 2023Article
- Deep ensemble learning for automated non-advanced AMD classification using optimized retinal layer segmentation and SD-OCT scans.Computers in biology and medicine · 2023Article
- Ensemble Learning of Multiple Models Using Deep Learning for Multiclass Classification of Ultrasound Images of Hepatic Masses.Bioengineering (Basel, Switzerland) · 2023Article
- A novel genetic-artificial neural network based age estimation system.Scientific reports · 2022Article
- Ensemble Capsule Network with an Attention Mechanism for the Fault Diagnosis of Bearings from Imbalanced Data Samples.Sensors (Basel, Switzerland) · 2022Article
- Remote Health Monitoring in Clinical Trial using Machine Learning Techniques: A Conceptual Framework.Health and technology · 2022Review
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tuberculosis has the most considerable death rate among diseases caused by a single micro-organism type. The disease is a significant issue for most third-world countries due to poor diagnosis and treatment potentials. Early diagnosis of tuberculosis is the most effective way of managing the disease in patients to reduce the mortality rate of the infection. Despite several methods that exist in diagnosing tuberculosis, the limitations ranging from the cost in carrying out the test to the time taken to obtain the results have hindered early diagnosis of the disease. This work aims to develop a predictive model that would help in the diagnosis of TB using an extended weighted voting ensemble method. The method used to carry out this research involved analyzing tuberculosis gene expression data obtained from GEO (Transcript Expression Omnibus) database and developing a classification model to aid tuberculosis diagnosis. A classifier combination of Naïve Bayes (NB), and Support Vector Machine (SVM) was used to develop the classification model. The weighted voting ensemble technique was used to improve the classification model's performance by combining the classification results of the single classifier and selecting the group with the highest vote based on the weights given to the single classifiers. Experimental analysis indicates a performance accuracy of the enhanced ensemble classifier as 0.95, which showed a better performance than the single classifiers, which had 0.92, and 0.87 obtained from SVM and NB, respectively. The developed model can also assist health practitioners in the timely diagnosis of tuberculosis, which would reduce the mortality rate caused by the disease, especially in developing countries.
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