Detection of Monkeypox Cases Based on Symptoms Using XGBoost and Shapley Additive Explanations Methods

Farzipour, Alireza; Elmi, Roya; Nasiri, Hamid

doi:10.3390/diagnostics13142391

Cited by 13 publications

(4 citation statements)

References 64 publications

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“…where isthe predicted value for the i th instance, K is the number of weak models, f k ( x i ) isthe output of the k th weak model on the i th instance, and x i isthe feature vector for the i th instance ( Farzipour, Elmi & Nasiri, 2023 ; Nasiri, Homafar & Chelgani, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Development of an expert system for the classification of myalgic encephalomyelitis/chronic fatigue syndrome

Yagin,

Shateri,

Nasiri

et al. 2024

PeerJ Computer Science

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Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a severe condition with an uncertain origin and a dismal prognosis. There is presently no precise diagnostic test for ME/CFS, and the diagnosis is determined primarily by the presence of certain symptoms. The current study presents an explainable artificial intelligence (XAI) integrated machine learning (ML) framework that identifies and classifies potential metabolic biomarkers of ME/CFS. Metabolomic data from blood samples from 19 controls and 32 ME/CFS patients, all female, who were between age and body mass index (BMI) frequency-matched groups, were used to develop the XAI-based model. The dataset contained 832 metabolites, and after feature selection, the model was developed using only 50 metabolites, meaning less medical knowledge is required, thus reducing diagnostic costs and improving prognostic time. The computational method was developed using six different ML algorithms before and after feature selection. The final classification model was explained using the XAI approach, SHAP. The best-performing classification model (XGBoost) achieved an area under the receiver operating characteristic curve (AUCROC) value of 98.85%. SHAP results showed that decreased levels of alpha-CEHC sulfate, hypoxanthine, and phenylacetylglutamine, as well as increased levels of N-delta-acetylornithine and oleoyl-linoloyl-glycerol (18:1/18:2)[2], increased the risk of ME/CFS. Besides the robustness of the methodology used, the results showed that the combination of ML and XAI could explain the biomarker prediction of ME/CFS and provided a first step toward establishing prognostic models for ME/CFS.

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Section: Methodsmentioning

confidence: 99%

Development of an expert system for the classification of myalgic encephalomyelitis/chronic fatigue syndrome

Yagin,

Shateri,

Nasiri

et al. 2024

PeerJ Computer Science

Self Cite

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“…In this research, PD detection was implemented using two classification models: the logistic classifier [ 35 ] and extreme gradient boosting (XGBoost) [ 36 ]. These two classification models have been used in various computer-assisted diagnosing applications [ 37 , 38 , 39 ].…”

Section: Materials and Methodsmentioning

confidence: 99%

Causal Forest Machine Learning Analysis of Parkinson’s Disease in Resting-State Functional Magnetic Resonance Imaging

Solana-Lavalle,

Cusimano,

Steeves

et al. 2024

Tomography

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In recent years, Artificial Intelligence has been used to assist healthcare professionals in detecting and diagnosing neurodegenerative diseases. In this study, we propose a methodology to analyze functional Magnetic Resonance Imaging signals and perform classification between Parkinson’s disease patients and healthy participants using Machine Learning algorithms. In addition, the proposed approach provides insights into the brain regions affected by the disease. The functional Magnetic Resonance Imaging from the PPMI and 1000-FCP datasets were pre-processed to extract time series from 200 brain regions per participant, resulting in 11,600 features. Causal Forest and Wrapper Feature Subset Selection algorithms were used for dimensionality reduction, resulting in a subset of features based on their heterogeneity and association with the disease. We utilized Logistic Regression and XGBoost algorithms to perform PD detection, achieving 97.6% accuracy, 97.5% F1 score, 97.9% precision, and 97.7%recall by analyzing sets with fewer than 300 features in a population including men and women. Finally, Multiple Correspondence Analysis was employed to visualize the relationships between brain regions and each group (women with Parkinson, female controls, men with Parkinson, male controls). Associations between the Unified Parkinson’s Disease Rating Scale questionnaire results and affected brain regions in different groups were also obtained to show another use case of the methodology. This work proposes a methodology to (1) classify patients and controls with Machine Learning and Causal Forest algorithm and (2) visualize associations between brain regions and groups, providing high-accuracy classification and enhanced interpretability of the correlation between specific brain regions and the disease across different groups.

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“…Here, λ i represents the Lagrange coefficients. In sample space problems that cannot be linearly separated, SVM uses kernel functions to move the sample space to another space where it can be linearly separated [30,31]. These functions are the Linear Kernel function, the Polynomial Kernel function, the Radial Basis Function (RBF), the Kernel function, and the Sigmoid Kernel function.…”

Section: Support Vector Machinesmentioning

confidence: 99%

A Comparative Analysis of Skin Cancer Detection Applications Using Histogram-Based Local Descriptors

Aydin

2023

Diagnostics

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Among the most serious types of cancer is skin cancer. Despite the risk of death, when caught early, the rate of survival is greater than 95%. This inspires researchers to explore methods that allow for the early detection of skin cancer that could save millions of lives. The ability to detect the early signs of skin cancer has become more urgent in light of the rising number of illnesses, the high death rate, and costly healthcare treatments. Given the gravity of these issues, experts have created a number of existing approaches for detecting skin cancer. Identifying skin cancer and whether it is benign or malignant involves detecting features of the lesions such as size, form, symmetry, color, etc. The aim of this study is to determine the most successful skin cancer detection methods by comparing the outcomes and effectiveness of the various applications that categorize benign and malignant forms of skin cancer. Descriptors such as the Local Binary Pattern (LBP), the Local Directional Number Pattern (LDN), the Pyramid of Histogram of Oriented Gradients (PHOG), the Local Directional Pattern (LDiP), and Monogenic Binary Coding (MBC) are used to extract the necessary features. Support vector machines (SVM) and XGBoost are used in the classification process. In addition, this study uses colored histogram-based features to classify the various characteristics obtained from the color images. In the experimental results, the applications implemented with the proposed color histogram-based features were observed to be more successful. Under the proposed method (the colored LDN feature obtained using the YCbCr color space with the XGBoost classifier), a 90% accuracy rate was achieved on Dataset 1, which was obtained from the Kaggle website. For the HAM10000 data set, an accuracy rate of 96.50% was achieved under a similar proposed method (the colored MBC feature obtained using the HSV color space with the XGBoost classifier).

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Detection of Monkeypox Cases Based on Symptoms Using XGBoost and Shapley Additive Explanations Methods

Cited by 13 publications

References 64 publications

Development of an expert system for the classification of myalgic encephalomyelitis/chronic fatigue syndrome

Development of an expert system for the classification of myalgic encephalomyelitis/chronic fatigue syndrome

Causal Forest Machine Learning Analysis of Parkinson’s Disease in Resting-State Functional Magnetic Resonance Imaging

A Comparative Analysis of Skin Cancer Detection Applications Using Histogram-Based Local Descriptors

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