Development and validation of a machine learning approach for screening new leprosy cases based on the leprosy suspicion questionnaire
Abstract Leprosy is a dermatoneurological disease and can cause irreversible nerve damage. In addition to being able to mimic different rheumatological, neurological and dermatological diseases, leprosy is underdiagnosed because several professionals present lack of training. The World Health Organi...
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Nature Portfolio
2025-02-01
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| Online Access: | https://doi.org/10.1038/s41598-025-91462-6 |
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| author | Mateus Mendonça Ramos Simões Filipe Rocha Lima Helena Barbosa Lugão Natália Aparecida de Paula Cláudia Maria Lincoln Silva Alexandre Ferreira Ramos Marco Andrey Cipriani Frade |
| author_facet | Mateus Mendonça Ramos Simões Filipe Rocha Lima Helena Barbosa Lugão Natália Aparecida de Paula Cláudia Maria Lincoln Silva Alexandre Ferreira Ramos Marco Andrey Cipriani Frade |
| author_sort | Mateus Mendonça Ramos Simões |
| collection | DOAJ |
| description | Abstract Leprosy is a dermatoneurological disease and can cause irreversible nerve damage. In addition to being able to mimic different rheumatological, neurological and dermatological diseases, leprosy is underdiagnosed because several professionals present lack of training. The World Health Organization instituted active search for new leprosy cases as one of the four pillars of the zero-leprosy strategy. The Leprosy Suspicion Questionnaire (LSQ) was created aiming to be a screening tool to actively detect new cases; it is composed of 14 simple yes/no questions that can be answered with the help of a health professional or by the very patient themselves. During its development, it was noticed that the combination of marked questions was related to new case detections. To better encapsulate and being able to expand its use, we developed MaLeSQs, a Machine Learning tool whose output may be LSQ Positive when the subject is indicated for being further clinically evaluated or LSQ Negative when the subject does not present any evidence that justify being further evaluated for leprosy. To achieve a reasonable product, we trained four classifiers with different learning paradigms, Support Vectors Machine, Logistic Regression, Random Forest and XGBoost. We compared them based on sensitivity, specificity, positive predicted value, negative predicted value, and area under the ROC curve. After the training process, the Support Vectors Machine was the classifier with the most balanced metrics of 85.7% sensitivity, 69.2% specificity, 18.6% precision, 98.3% negative predicted values and an area under the ROC curve of 0.775, and it was chosen as the MaLeSQs. With Shapley values, we were able to evaluate variable importance and nerve symptoms were considered important to differentiate between subjects that potentially had leprosy from those who did not. |
| format | Article |
| id | doaj-art-b8f192b331d248ac9aa578d5f5e6aa1b |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-b8f192b331d248ac9aa578d5f5e6aa1b2025-08-20T03:04:34ZengNature PortfolioScientific Reports2045-23222025-02-0115111610.1038/s41598-025-91462-6Development and validation of a machine learning approach for screening new leprosy cases based on the leprosy suspicion questionnaireMateus Mendonça Ramos Simões0Filipe Rocha Lima1Helena Barbosa Lugão2Natália Aparecida de Paula3Cláudia Maria Lincoln Silva4Alexandre Ferreira Ramos5Marco Andrey Cipriani Frade6Dermatology Division, Department of Internal Medicine, National Referral Center for Sanitary Dermatology and Hansen’s Disease, Clinical Hospital of the Ribeirão Preto Medical School, University of São PauloDermatology Division, Department of Internal Medicine, National Referral Center for Sanitary Dermatology and Hansen’s Disease, Clinical Hospital of the Ribeirão Preto Medical School, University of São PauloDermatology Division, Department of Internal Medicine, National Referral Center for Sanitary Dermatology and Hansen’s Disease, Clinical Hospital of the Ribeirão Preto Medical School, University of São PauloDermatology Division, Department of Internal Medicine, National Referral Center for Sanitary Dermatology and Hansen’s Disease, Clinical Hospital of the Ribeirão Preto Medical School, University of São PauloDermatology Division, Department of Internal Medicine, National Referral Center for Sanitary Dermatology and Hansen’s Disease, Clinical Hospital of the Ribeirão Preto Medical School, University of São PauloArts, Science and Humanities School, University of São PauloDermatology Division, Department of Internal Medicine, National Referral Center for Sanitary Dermatology and Hansen’s Disease, Clinical Hospital of the Ribeirão Preto Medical School, University of São PauloAbstract Leprosy is a dermatoneurological disease and can cause irreversible nerve damage. In addition to being able to mimic different rheumatological, neurological and dermatological diseases, leprosy is underdiagnosed because several professionals present lack of training. The World Health Organization instituted active search for new leprosy cases as one of the four pillars of the zero-leprosy strategy. The Leprosy Suspicion Questionnaire (LSQ) was created aiming to be a screening tool to actively detect new cases; it is composed of 14 simple yes/no questions that can be answered with the help of a health professional or by the very patient themselves. During its development, it was noticed that the combination of marked questions was related to new case detections. To better encapsulate and being able to expand its use, we developed MaLeSQs, a Machine Learning tool whose output may be LSQ Positive when the subject is indicated for being further clinically evaluated or LSQ Negative when the subject does not present any evidence that justify being further evaluated for leprosy. To achieve a reasonable product, we trained four classifiers with different learning paradigms, Support Vectors Machine, Logistic Regression, Random Forest and XGBoost. We compared them based on sensitivity, specificity, positive predicted value, negative predicted value, and area under the ROC curve. After the training process, the Support Vectors Machine was the classifier with the most balanced metrics of 85.7% sensitivity, 69.2% specificity, 18.6% precision, 98.3% negative predicted values and an area under the ROC curve of 0.775, and it was chosen as the MaLeSQs. With Shapley values, we were able to evaluate variable importance and nerve symptoms were considered important to differentiate between subjects that potentially had leprosy from those who did not.https://doi.org/10.1038/s41598-025-91462-6LeprosyLeprosy suspicion questionnaireMachine learningScreeningActive search |
| spellingShingle | Mateus Mendonça Ramos Simões Filipe Rocha Lima Helena Barbosa Lugão Natália Aparecida de Paula Cláudia Maria Lincoln Silva Alexandre Ferreira Ramos Marco Andrey Cipriani Frade Development and validation of a machine learning approach for screening new leprosy cases based on the leprosy suspicion questionnaire Scientific Reports Leprosy Leprosy suspicion questionnaire Machine learning Screening Active search |
| title | Development and validation of a machine learning approach for screening new leprosy cases based on the leprosy suspicion questionnaire |
| title_full | Development and validation of a machine learning approach for screening new leprosy cases based on the leprosy suspicion questionnaire |
| title_fullStr | Development and validation of a machine learning approach for screening new leprosy cases based on the leprosy suspicion questionnaire |
| title_full_unstemmed | Development and validation of a machine learning approach for screening new leprosy cases based on the leprosy suspicion questionnaire |
| title_short | Development and validation of a machine learning approach for screening new leprosy cases based on the leprosy suspicion questionnaire |
| title_sort | development and validation of a machine learning approach for screening new leprosy cases based on the leprosy suspicion questionnaire |
| topic | Leprosy Leprosy suspicion questionnaire Machine learning Screening Active search |
| url | https://doi.org/10.1038/s41598-025-91462-6 |
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