Taxonomy on Healthcare System Based on Machine Learning Approaches: Tuberculosis Disease Diagnosis

Karmani, Priyanka; Chandio, Aftab Ahmed; Karmani, Vivekanand; Soomro, Javed Ali; Korejo, Imtiaz Ali; Chandio, M. S.

doi:10.12785/ijcds/0906017

Cited by 4 publications

(4 citation statements)

References 61 publications

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“…2) Tools: Regarding XAI tools for healthcare, we have investigated three major points: developed tools, their use cases, and their effectiveness in enhancing transparency and interpretability. First, this study overviews several XAI tools such as LIME, ILIME, SHAP, and MAPLE [57]. These tools are agnostic to the underlying machine learning models and can be used to gain insights into the decision-making processes of these models.…”

Section: B Discussionmentioning

confidence: 99%

“…These taxonomies assist healthcare professionals in choosing appropriate interpretability methods that meet the specific demands of medical applications, promoting greater alignment with clinical needs and improving patient outcomes, as highlighted by [53], [54]. The suties proposed by [55], [56], [57], highlight how AI models may be used to understand the operational properties of AI models, build confidence, and make educated healthcare decisions. Taxonomies and classifications aid in clearly and safely incorporating AI models into healthcare systems, particularly when decision accuracy is crucial and directly impacts patient health [58].…”

Section: ) Rq1mentioning

confidence: 99%

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A Systematic Literature Review on Transparencyand Interpretability of AI models in Healthcare:Taxonomies, Tools, Techniques, Datasets, OpenResearch Challenges, and Future Trends

Shafik,

Hidayatullah,

Kalinaki

et al. 2024

Preprint

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The increased utilization of disruptive health and biomedical informatics technologies, such as artificial intelligence (AI), has accelerated medical operations from patient-centered medical experience data management to simplified medical procedures in this generative era. As these technologies integrate into traditional approaches, they raise critical medical concerns, entailing transparency and interpretability of these AI models. This study conducts a systematic literature review (SLR) and presents an exhaustive review of the studies using data collection procedures and publicly available academic databases. 1837 articles published between 2014 and 2024 were obtained from eight popular academic databases: PubMed, ACM Library, Springer, Scopus, IEEE Xplore, ScienceDirect, Google Scholar, and Web of Science. A comprehensive screening process was used, and 148 articles were considered based on the relevance of the AI method to healthcare and biomedical. The studied studies demonstrate that the majority of medical people still find it complex to effectively explain the reasoning behind the decisions AI models make during biomedical experiments, leading to limited trust, biased decision-making, and unknown patient data safety. Model-agnostic strategies and explainable AI (XAI) frameworks are inspected, together with crucial datasets for training and assessment. The main challenges are AI model intricacy and regulatory compliance, while future trends highlight fairness and predisposition mitigation. Limited studies are focusing on improving AI openness, trust, and interpretability. Towards the end, it reveals that there is still a big research gap in descriptive explainable AI models in healthcare when integrating AI into clinical practice while maintaining ethical standards and patient-centric care.

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Section: B Discussionmentioning

confidence: 99%

Section: ) Rq1mentioning

confidence: 99%

A Systematic Literature Review on Transparencyand Interpretability of AI models in Healthcare:Taxonomies, Tools, Techniques, Datasets, OpenResearch Challenges, and Future Trends

Shafik,

Hidayatullah,

Kalinaki

et al. 2024

Preprint

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“…In such a case, the algorithm learns from an elementary structure in the data to identify and give an interesting pattern [49]. The main difference between supervised and unsupervised learning is that unsupervised learning does not use a feedback signal to examine the standard solutions, making it less accurate and computationally complex [50]. In recent years, supervised and unsupervised learning was used in Bogota, DC, Colombia, to build two models using an artificial neural network to diagnose the disease and cluster data.…”

Section: Overview Of Ai Techniques Used In Tb Diagnosismentioning

confidence: 99%

Evolution of Machine Learning in Tuberculosis Diagnosis: A Review of Deep Learning-Based Medical Applications

et al. 2022

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Tuberculosis (TB) is an infectious disease that has been a major menace to human health globally, causing millions of deaths yearly. Well-timed diagnosis and treatment are an arch to full recovery of the patient. Computer-aided diagnosis (CAD) has been a hopeful choice for TB diagnosis. Many CAD approaches using machine learning have been applied for TB diagnosis, specific to the artificial intelligence (AI) domain, which has led to the resurgence of AI in the medical field. Deep learning (DL), a major branch of AI, provides bigger room for diagnosing deadly TB disease. This review is focused on the limitations of conventional TB diagnostics and a broad description of various machine learning algorithms and their applications in TB diagnosis. Furthermore, various deep learning methods integrated with other systems such as neuro-fuzzy logic, genetic algorithm, and artificial immune systems are discussed. Finally, multiple state-of-the-art tools such as CAD4TB, Lunit INSIGHT, qXR, and InferRead DR Chest are summarized to view AI-assisted future aspects in TB diagnosis.

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“…Additionally, the lack of a widely recognized benchmark for assessing nutritional status and the underdiagnosis of malnutrition underscore the importance of implementing systematic screening and intervention measures in healthcare practices. [7][8][9][10] II RELATED WORK Ahsan et al [11] to use the VGG16 DL classification network, two well-known datasets were put together. In the dataset for Montgomery County, there are 58 T.B.…”

Section: Introductionmentioning

confidence: 99%

A-Novel in this Technique Analysis Tuberculosis Patient Using Hybrid CNN and SVM Model

Nisha

2023

IJRITCC

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Tuberculosis (TB) remains a global health challenge with a significant impact on public health worldwide. This study addresses the prevalence and detection of T.B., focusing on India, a country experiencing rapid economic growth and notable transitions in various sectors. Despite advancements in healthcare delivery, communicable and non-communicable diseases, including TB, continue to pose substantial threats to health security. The proposed method for T.B. analysis integrates key modules, encompassing data collection, preprocessing, feature extraction, image splitting, classification, and performance estimation. Utilizing machine learning algorithms such as Support Vector Machine (SVM) and Convolutional Neural Network (CNN), the study aims to enhance accuracy and efficiency in T.B. patient detection. The dataset includes chest X-rays from diverse sources, aiming to provide a comprehensive understanding of T.B. patterns. The study emphasizes the interconnected challenges of malnutrition and T.B., highlighting the importance of nutritional status assessment in public health. The linkage between undernutrition and infectious diseases, including TB, underscores the need for early detection and intervention. The proposed method offers a systematic approach to evaluating and addressing nutritional deficiencies in patients with chronic illnesses, contributing to improved clinical outcomes.

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Taxonomy on Healthcare System Based on Machine Learning Approaches: Tuberculosis Disease Diagnosis

Cited by 4 publications

References 61 publications

A Systematic Literature Review on Transparencyand Interpretability of AI models in Healthcare:Taxonomies, Tools, Techniques, Datasets, OpenResearch Challenges, and Future Trends

A Systematic Literature Review on Transparencyand Interpretability of AI models in Healthcare:Taxonomies, Tools, Techniques, Datasets, OpenResearch Challenges, and Future Trends

Evolution of Machine Learning in Tuberculosis Diagnosis: A Review of Deep Learning-Based Medical Applications

A-Novel in this Technique Analysis Tuberculosis Patient Using Hybrid CNN and SVM Model

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