ArticleInfectious Disease Modelling2022
Feature importance: Opening a soil-transmitted helminth machine learning model via SHAP.
Article in Infectious Disease Modelling, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Development and validation of an interpretable Random Forest model for predicting recurrence after endoscopic submucosal dissection in superficial oesophageal squamous cell carcinoma.Annals of medicine · 2026Article
- Health Care Access Barriers Among Reproductive-Age Women in East Africa: Development and Validation of Machine Learning Prediction Models Using DHS Data.JMIR medical informatics · 2026Article
- Potential of machine learning for prevention and control of neglected tropical diseases: a scoping review.Communications medicine · 2026Article
- Carbon Emission Intensity Modeling in Freight Operations Using eXplainable AI in Real-World Conditions.ACS omega · 2026Article
- A novel hybrid model for species distribution prediction of soil-transmitted helminthiasis (STH) under soil temperature conditions using Random Forest and Particle Swarm Optimization Algorithm.Scientific reports · 2026Article
- Article
- Predicting plaque-gingivitis risk in schoolchildren using an interpretable machine learning model: a cross-sectional study.BMC oral health · 2025Article
- Identifying patterns of high intraoperative blood pressure variability in noncardiac surgery using explainable machine learning: a retrospective cohort study.Annals of medicine · 2025Article
- Optimizing prediction of metastasis among colorectal cancer patients using machine learning technology.BMC gastroenterology · 2025Article
- Predicting Superaverage Length of Stay in COPD Patients with Hypercapnic Respiratory Failure Using Machine Learning.Journal of inflammation research · 2025Article
- Constructing a Predictive Model for STH and Schistosomiasis Classification From Microscopic Images.BioMed research international · 2025Article
- Optimized Machine Learning Model for Predicting Compressive Strength of Alkali-Activated Concrete Through Multi-Faceted Comparative Analysis.Materials (Basel, Switzerland) · 2024Article
- Machine Learning Supported the Modified Gustafson's Criteria for Dental Age Estimation in Southwest China.Journal of imaging informatics in medicine · 2024Article
- Identification of key immune cells infiltrated in lung adenocarcinoma microenvironment and their related long noncoding RNA.iScience · 2024Article
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- Long-term gridded land evapotranspiration reconstruction using Deep Forest with high generalizability.Scientific data · 2023Article
- Disulfidptosis-associated lncRNAs predict breast cancer subtypes.Scientific reports · 2023Article
- Article
- Predicting risk of obesity in overweight adults using interpretable machine learning algorithms.Frontiers in endocrinology · 2023Article
Corrections and comments
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
In the field of landscape epidemiology, the contribution of machine learning (ML) to modeling of epidemiological risk scenarios presents itself as a good alternative. This study aims to break with the "black box" paradigm that underlies the application of automatic learning techniques by using SHAP to determine the contribution of each variable in ML models applied to geospatial health, using the prevalence of hookworms, intestinal parasites, in Ethiopia, where they are widely distributed; the country bears the third-highest burden of hookworm in Sub-Saharan Africa. XGBoost software was used, a very popular ML model, to fit and analyze the data. The Python SHAP library was used to understand the importance in the trained model, of the variables for predictions. The description of the contribution of these variables on a particular prediction was obtained, using different types of plot methods. The results show that the ML models are superior to the classical statistical models; not only demonstrating similar results but also explaining, by using the SHAP package, the influence and interactions between the variables in the generated models. This analysis provides information to help understand the epidemiological problem presented and provides a tool for similar studies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.