Enhancing Tuberculosis Diagnosis and Treatment Outcomes: A Stacked Loopy Decision Tree Approach Empowered by Moth Search Algorithm Optimization

• Published in: International journal of advanced computer science & applications Vol. 15; no. 8
• Main Authors: Khan, Huma, DSouza, Mithun, Babu, K. Suresh, Ramesh, Janjhyam Venkata Naga, Praneeth, K. R., Rao, Pinapati Lakshmana
• Format: Journal Article
• Language: English
• Published: West Yorkshire Science and Information (SAI) Organization Limited 2024
• Subjects: Abnormalities; Accuracy; Business metrics; Complexity; Decision trees; Diagnosis; Image enhancement; Machine learning; Medical imaging; Performance evaluation; Performance measurement; Search algorithms; Tuberculosis; X ray imagery
• ISSN: 2158-107X; 2156-5570; 2156-5570
• DOI: 10.14569/IJACSA.2024.0150884

Chest X-ray imaging is the main tool for detecting tuberculosis (TB), providing essential information about pulmonary abnormalities that may indicate the presence of the disease. Still, manual interpretation is a common component of older diagnostic methods, and it may be laborious and subjective. The development of sophisticated machine learning methods offers a potential way to improve TB detection through the automation of chest X-ray image interpretation. This takes a look at goals to increase a sturdy framework for TB diagnosis the usage of Stacked Loopy Decision Trees (SLDT) optimized with the Moth Search Algorithm (MSA). The objective is to improve upon current techniques with the aid of integrating sophisticated feature extraction and ensemble mastering strategies. The novelty lies in the integration of SLDT, a hierarchical ensemble model able to shooting complex styles in chest X-ray images, with MSA for optimized parameter tuning and function selection. This technique addresses the complexity of TB analysis by enhancing each interpretability and overall performance metrics. The proposed framework employs the Gray-Level Co-prevalence Matrix (GLCM) for texture characteristic extraction, accompanied with the aid of SLDT ensemble studying optimized through MSA. This methodology objectives to discern TB-particular styles from chest X-ray pictures with excessive accuracy and efficiency. Evaluation of a comprehensive dataset demonstrates advanced performance metrics including accuracy, sensitivity, specificity, and vicinity underneath the ROC curve (AUC-ROC) compared to traditional gadget gaining knowledge of procedures. The outcomes demonstrate how well the SLDT-MSA framework performs in diagnosing TB, with 99% accuracy. The observation indicates that the SLDT-MSA framework offers practitioners a trustworthy and easily understandable solution, marking a significant advancement in TB diagnosis.