Interpretability analysis for thermal sensation machine learning models: An exploration based on the SHAP approach

Yang, Yu-Ren; Yuan, Ye; Han, Zhen; Liu, Gang

doi:10.1111/ina.12984

Cited by 48 publications

(21 citation statements)

References 52 publications

(183 reference statements)

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“…The risk prediction model for detection of early sepsis using the transcriptome data-based method and the CatBoost method showed good performance in terms of model validity and clinical net benefit. However, the black-box properties of machine learning models result in a lack of model transparency, and existing explanations for the models are flawed in terms of the interpretation of the methods ( Yang et al, 2022 ).…”

Section: Resultsmentioning

confidence: 99%

“…The KNN algorithm ( Bania and Halder, 2020 ) was used to fill in the missing data. The preprocessed data set was divided into training and test sets in a ratio of 2:1 (274 for the training set and 118 for the test set) ( Fabian et al, 2011 ; Yang et al, 2022 ).…”

Section: Methodsmentioning

confidence: 99%

“…Shapley Additive explanation (SHAP) package ( Lundberg and Lee, 2017 ), a method for uniform measure of the feature importance in machine learning models, was used for the visualization and explanation of the prediction model. Explanations based on the Shapley value have a solid theoretical foundation; it is the only attribution method that satisfies the requirements of local accuracy, missingness, and consistency ( Lundberg et al, 2020 ; Yang et al, 2022 ). However, the Shapley value provides only a local explanation ( Yang et al, 2022 ).…”

Section: Methodsmentioning

confidence: 99%

“…Explanations based on the Shapley value have a solid theoretical foundation; it is the only attribution method that satisfies the requirements of local accuracy, missingness, and consistency ( Lundberg et al, 2020 ; Yang et al, 2022 ). However, the Shapley value provides only a local explanation ( Yang et al, 2022 ). The SHAP package developed by Lundberg et al based on the Shapley value inherits all of the advantages of the Shapley value ( Lundberg and Lee, 2017 ; Yang et al, 2022 ).…”

Section: Methodsmentioning

confidence: 99%

“…However, the Shapley value provides only a local explanation ( Yang et al, 2022 ). The SHAP package developed by Lundberg et al based on the Shapley value inherits all of the advantages of the Shapley value ( Lundberg and Lee, 2017 ; Yang et al, 2022 ). Making the global analysis consistent with the local explanation, SHAP has been used to perform interpretability analysis by different researchers in their respective fields ( Lundberg and Lee, 2017 ; Lundberg et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

See 4 more Smart Citations

An application based on bioinformatics and machine learning for risk prediction of sepsis at first clinical presentation using transcriptomic data

et al. 2022

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Background: Linking genotypic changes to phenotypic traits based on machine learning methods has various challenges. In this study, we developed a workflow based on bioinformatics and machine learning methods using transcriptomic data for sepsis obtained at the first clinical presentation for predicting the risk of sepsis. By combining bioinformatics with machine learning methods, we have attempted to overcome current challenges in predicting disease risk using transcriptomic data.Methods: High-throughput sequencing transcriptomic data processing and gene annotation were performed using R software. Machine learning models were constructed, and model performance was evaluated by machine learning methods in Python. The models were visualized and interpreted using the Shapley Additive explanation (SHAP) method.Results: Based on the preset parameters and using recursive feature elimination implemented via machine learning, the top 10 optimal genes were screened for the establishment of the machine learning models. In a comparison of model performance, CatBoost was selected as the optimal model. We explored the significance of each gene in the model and the interaction between each gene through SHAP analysis.Conclusion: The combination of CatBoost and SHAP may serve as the best-performing machine learning model for predicting transcriptomic and sepsis risks. The workflow outlined may provide a new approach and direction in exploring the mechanisms associated with genes and sepsis risk.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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An application based on bioinformatics and machine learning for risk prediction of sepsis at first clinical presentation using transcriptomic data

et al. 2022

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Breast cancer diagnosis and management guided by data augmentation, utilizing an integrated framework of SHAP and random augmentation

Ejiyi,

Qin,

Monday

et al. 2023

BioFactors

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Recent research indicates that early detection of breast cancer (BC) is critical in achieving favorable treatment outcomes and reducing the mortality rate associated with it. With the difficulty in obtaining a balanced dataset that is primarily sourced for the diagnosis of the disease, many researchers have relied on data augmentation techniques, thereby having varying datasets with varying quality and results. The dataset we focused on in this study is crafted from SHapley Additive exPlanations (SHAP)‐augmentation and random augmentation (RA) approaches to dealing with imbalanced data. This was carried out on the Wisconsin BC dataset and the effectiveness of this approach to the diagnosis of BC was checked using six machine‐learning algorithms. RA synthetically generated some parts of the dataset while SHAP helped in assessing the quality of the attributes, which were selected and used for the training of the models. The result from our analysis shows that the performance of the models used generally increased to more than 3% for most of the models using the dataset obtained by the integration of SHAP and RA. Additionally, after diagnosis, it is important to focus on providing quality care to ensure the best possible outcomes for patients. The need for proper management of the disease state is crucial so as to reduce the recurrence of the disease and other associated complications. Thus the interpretability provided by SHAP enlightens the management strategies in this study focusing on the quality of care given to the patient and how timely the care is.

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Multi‐feature, Chinese–Western medicine‐integrated prediction model for diabetic peripheral neuropathy based on machine learning and SHAP

Jiang,

Li,

Wang

et al. 2024

Diabetes Metabolism Res

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BackgroundClinical studies have shown that diabetic peripheral neuropathy (DPN) has been on the rise, with most patients presenting with severe and progressive symptoms. Currently, most of the available prediction models for DPN are derived from general clinical information and laboratory indicators. Several Traditional Chinese medicine (TCM) indicators have been utilised to construct prediction models. In this study, we established a novel machine learning‐based multi‐featured Chinese–Western medicine‐integrated prediction model for DPN using clinical features of TCM.Materials and MethodsThe clinical data of 1581 patients with Type 2 diabetes mellitus (T2DM) treated at the Department of Endocrinology of the First Affiliated Hospital of Anhui University of Chinese Medicine were collected. The data (including general information, laboratory parameters and TCM features) of 1142 patients with T2DM were selected after data cleaning. After baseline description analysis of the variables, the data were divided into training and validation sets. Four prediction models were established and their performance was evaluated using validation sets. Meanwhile, the accuracy, precision, recall, F1 score and area under the curve (AUC) of ROC were calculated using ten‐fold cross‐validation to further assess the performance of the models. An explanatory analysis of the results of the DPN prediction model was carried out using the SHAP framework based on machine learning‐based prediction models.ResultsOf the 1142 patients with T2DM, 681 had a comorbidity of DPN, while 461 did not. There was a significant difference between the two groups in terms of age, cause of disease, systolic pressure, HbA1c, ALT, RBC, Cr, BUN, red blood cells in the urine, glucose in the urine, and protein in the urine (p < 0.05). T2DM patients with a comorbidity of DPN exhibited diverse TCM symptoms, including limb numbness, limb pain, hypodynamia, thirst with desire for drinks, dry mouth and throat, blurred vision, gloomy complexion, and unsmooth pulse, with statistically significant differences (p < 0.05). Our results showed that the proposed multi‐featured Chinese–Western medicine‐integrated prediction model was superior to conventional models without characteristic TCM indicators. The model showed the best performance (accuracy = 0.8109, precision = 0.8029, recall = 0.9060, F1 score = 0.8511, and AUC = 0.9002). SHAP analysis revealed that the dominant risk factors that caused DPN were TCM symptoms (limb numbness, thirst with desire for drinks, blurred vision), age, cause of disease, and glycosylated haemoglobin. These risk factors were exerted positive effects on the DPN prediction models.ConclusionsA multi‐feature, Chinese–Western medicine‐integrated prediction model for DPN was established and validated. The model improves early‐stage identification of high‐risk groups for DPN in the diagnosis and treatment of T2DM, while also providing informative support for the intelligent management of chronic conditions such as diabetes.

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Interpretability analysis for thermal sensation machine learning models: An exploration based on the SHAP approach

Cited by 48 publications

References 52 publications

An application based on bioinformatics and machine learning for risk prediction of sepsis at first clinical presentation using transcriptomic data

An application based on bioinformatics and machine learning for risk prediction of sepsis at first clinical presentation using transcriptomic data

Breast cancer diagnosis and management guided by data augmentation, utilizing an integrated framework of SHAP and random augmentation

Multi‐feature, Chinese–Western medicine‐integrated prediction model for diabetic peripheral neuropathy based on machine learning and SHAP

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