ArticleEnvironmental science and pollution research international2019
Performance of machine-learning algorithms to pattern recognition and classification of hearing impairment in Brazilian farmers exposed to pesticide and/or cigarette smoke.
Article in Environmental science and pollution research international, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
12 citing papers in PubMed, 41 citations in OpenAlex.
- Personalized Hearing Loss Care Using SNOMED CT-Aligned Ontology and Random Forest Machine Learning: A Hybrid Decision-Support Framework.Audiology research · 2026Article
- AI-Driven Innovations in Hearing Health: A Review of Artificial Intelligence Applications in Audiology and Hearing Technologies.Current aging science · 2026Review
- Article
- Which Environmental Pollutants Are Toxic to Our Ears?-Evidence of the Ototoxicity of Common Substances.Toxics · 2024Review
- Application of Machine Learning Algorithms to Analyze the Clinical Characteristics of NIHL Caused by Impulse Noise and Steady Noise.Iranian journal of public health · 2024Article
- The value of machine learning for prognosis prediction of diphenhydramine exposure: National analysis of 50,000 patients in the United States.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2023Article
- Article
- Health Effects of Pesticide Exposure in Latin American and the Caribbean Populations: A Scoping Review.Environmental health perspectives · 2022Article
- Impact of Pesticides on Human Health in the Last Six Years in Brazil.International journal of environmental research and public health · 2022Review
- Exploring the effects and mechanisms of organophosphorus pesticide exposure and hearing loss.Frontiers in public health · 2022Review
- Profiling hearing aid users through big data explainable artificial intelligence techniques.Frontiers in neurology · 2022Article
- Contributions and limitations of using machine learning to predict noise-induced hearing loss.International archives of occupational and environmental health · 2021Review
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of pesticides has been increasing in agriculture, leading to a public health problem. The aim of this study was to evaluate ototoxic effects in farmers who were exposed to cigarette smoke and/or pesticides and to identify possible classification patterns in the exposure groups. The sample included 127 participants of both sexes aged between 18 and 39, who were divided into the following four groups: control group (CG), smoking group (SG), pesticide group (PG), and smoking + pesticide group (SPG). Meatoscopy, pure tone audiometry, logoaudiometry, high-frequency thresholds, and immittance testing were performed. Data were evaluated by artificial neural network (ANN), K-nearest neighbors (K-NN), and support vector machine (SVM). There was symmetry between the right and left ears, an increase in the incidence of hearing loss at high frequency and of downward sloping audiometric curve configuration, and alteration of stapedial reflex in the three exposed groups. The machine-learning classifiers achieved good classification performance (control and exposed). The best classification results occur in high type (I and II) datasets (about 90% accuracy) in k-NN test. It is concluded that both xenobiotic substances have ototoxic potential; however, their combined use does not present additive or potentiating effects recognizable by the algorithms.
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