Performance of machine-learning algorithms to pattern recognition and classification of hearing impairment in Brazilian farmers exposed to pesticide and/or cigarette smoke
- 22 Downloads
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.
KeywordsMachine learning Artificial intelligence Pesticide Smoking Hearing loss Farmer
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. Grant support was provided by University of Western São Paulo (UNOESTE).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- Abdollahi H, Mostafaei S, Cheraghi S, Shiri I, Rabi Mahdavi S, Kazemnejad A (2018) Cochlea CT radiomics predicts chemoradiotherapy induced sensorineural hearing loss in head and neck cancer patients: a machine learning and multi-variable modelling study. Phys Med 45:192–197. https://doi.org/10.1016/j.ejmp.2017.10.008 CrossRefGoogle Scholar
- Benedetti D, Nunes E, Sarmento M, Porto C, Dos Santos CE, Dias JF, da Silva J (2013) Genetic damage in soybean workers exposed to pesticides: evaluation with the comet and buccal micronucleus cytome assays. Mutat Res 752(1–2):28–33. https://doi.org/10.1016/j.mrgentox.2013.01.001 CrossRefGoogle Scholar
- Chang CC, Lin CJ (2011) LIBSVM: A library for support vector machines. ACM Trans Intell Syst Technol 2:1–27 Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm. Accessed 10 Nov 2017
- Crawford JM, Hoppin JA, Alavania MC, Blair A, Sandler DP, Kamel F (2008) Hearing loss among licensed pesticide applicators in the agricultural health study running title: hearing loss among licensed pesticide applicators. J Occup Environ Med 7:817–826. https://doi.org/10.1097/JOM.0b013e31816a8caf CrossRefGoogle Scholar
- Deziel NC, Beane Freeman LE, Graubard BI, Jones RR, Hoppin JA, Thomas K, Hines CJ, Blair A, Sandler DP, Chen H, Lubin JH, Andreotti G, Alavanja MC, Friesen MC (2017) Relative contributions of agricultural drift, Para-occupational, and residential use exposure pathways to house dust pesticide concentrations: meta-regression of published data. Environ Health Perspect 125(3):296–305. https://doi.org/10.1289/EHP426 CrossRefGoogle Scholar
- Gelfand SA (1984) The contralateral acoustic reflex threshold. In: Silman S (ed) The acoustic reflex: basic principles and clinical aplications, Academic Press, Orlando, pp 137–186Google Scholar
- Haykin S (1999) Neural networks: a Comprehensive Foundation, 2nd edn. Prentice Hall, New JerseyGoogle Scholar
- Jerger J (1970) Clinical experience with impedance audiometry. Arch Otolaryngol 92(4):311–324. https://doi.org/10.1001/archotol.1970.04310040005002 CrossRefGoogle Scholar
- Lloyd LL, Kaplan H (1978) Audiometric interpretation: a manual of basic audiometry. University Park Press, BaltimoreGoogle Scholar
- Nissen S (2003) Implementation of a Fast Artificial Neural Network Library (FANN). Department of Computer Science University of Copenhagen (DIKU), Copenhagen Software available at http://leenissen.dk/fann/. Accessed 09 Nov 2017
- Schölkopf B, Smola A (2002) Learning with kernels. MIT Press, CambridgeGoogle Scholar
- Sharabi Y, Reshef-Haran I, Burstein M, Eldad A (2002) Cigarette smoking and hearing loss: lessons from the young adults periodic examinations in Israel (YAPEIS) database. Isr Med Assoc J 4(12):1118–1120Google Scholar
- Silman S, Silverman CA (1997) Basic audiologic testing. In: Silman S, Silverman CA (eds) Auditory diagnosis: principles and applications. Singular Publishing Group, San Diego, pp 44–52Google Scholar
- Silvério ACP, Machado SC, Azevedo L, Nogueira DA, de Castro Graciano MM, Simões JS, Viana ALM, Martins I (2017) Assessment of exposure to pesticides in rural workers in southern of Minas Gerais, Brazil. Environ Toxicol Pharmacol 55:99–106. https://doi.org/10.1016/j.etap.2017.08.013 CrossRefGoogle Scholar
- Tomiazzi JS, Judai MA, Nai GA, Pereira DR, Antunes PA, Favareto APA (2018) Evaluation of genotoxic effects in Brazilian agricultural workers exposed to pesticides and cigarette smoke using machine-learning algorithms. Environ Sci Pollut Res Int 25(2):1259–1269. https://doi.org/10.1007/s11356-017-0496-y CrossRefGoogle Scholar
- Zhao X, Zhang X, Cai Z, Tian X, Wang X, Huang Y, Chen H, Hu L (2018) Chaos enhanced grey wolf optimization wrapped ELM for diagnosis of paraquat-poisoned patients. Comput Biol Chem. https://doi.org/10.1016/j.compbiolchem.2018.11.017