Use of data mining to predict significant factors and benefits of bilateral cochlear implantation

Ramos-Miguel, Angel; Perez-Zaballos, Teresa; Pérez, Daniel; Falconb, Juan Carlos; Ramosb, Angel

doi:10.1007/s00405-014-3337-3

Cited by 14 publications

(11 citation statements)

References 10 publications

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“…The general stages in orders are as the following: 1) choosing the predictive factors; 2) performing the audiometry and calculating the permanent hearing loss of each ear and then overall hearing loss; 3) classifying the types of hearing loss (It should be noted that the audiometric data are divided into two parts, teaching and evaluation. In teaching part 40 people were selected from each group and so total were 120 persons, and in the evaluation part, 10 persons were selected from each group that subsequently amounted to 30 persons); 4) determining the weight of factors effective on the hearing loss based on the C5 algorithm; and 5) determining the error and accuracy rate [21]. Four variables including age, work experience, the equivalent sound level and frequency were considered for each person [2122].…”

Section: Methodsmentioning

confidence: 99%

“…In teaching part 40 people were selected from each group and so total were 120 persons, and in the evaluation part, 10 persons were selected from each group that subsequently amounted to 30 persons); 4) determining the weight of factors effective on the hearing loss based on the C5 algorithm; and 5) determining the error and accuracy rate [21]. Four variables including age, work experience, the equivalent sound level and frequency were considered for each person [2122]. All participants were adults and were divided three age groups; the first age range was between 20 and 35 years, the second range was between 35 and 50 years, and the third age range was above 50 years [21].…”

Section: Methodsmentioning

confidence: 99%

“…Four variables including age, work experience, the equivalent sound level and frequency were considered for each person [2122]. All participants were adults and were divided three age groups; the first age range was between 20 and 35 years, the second range was between 35 and 50 years, and the third age range was above 50 years [21]. In terms of the work experience, they were divided three ranges with a work experience of fewer than 10 years, between 10 to 20 years and over 20 years [20].…”

Section: Methodsmentioning

confidence: 99%

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Predicting and Weighting the Factors Affecting Workers’ Hearing Loss Based on Audiometric Data Using C5 Algorithm

Ghotbi-Ravandi

ElahiShirvan

Ahsaee

et al. 2019

Annals of Global Health

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Introduction: With the extensively spread of industrialization in the world, noise exposure is becoming more prevalent in the industrial settings. The most important and definite harmful effects of sound include hearing loss, both permanent and temporary. Objective: This study was designed aimed to use the C5 algorithm to determine the weight of factors affecting the workers’ hearing loss based on the audiometric data. Methods: This cross-sectional, descriptive, analytical study was conducted in 2018 in a mining industry in southeastern Iran. In this study, workers were divided into three exposed groups with different sound pressure levels (one control group and two case groups). Audiometry was conducted for each group of 50 persons; hence, the total number of subjects was 150. The stages of this study include: 1) selecting factors (predictive) to check and weigh them; 2) conducting the audiometry for both ears; 3) calculating the permanent hearing loss in each ear and permanent hearing loss of both ears; 4) classifying the types of hearing loss; and 5) investigating and determining the weight of factors affecting the hearing loss and their classification based on the C5 algorithm and determining the error and accuracy rate of each model. To assess and determine the factors affecting the hearing loss of workers, the C5 algorithm and IBM SPSS Modeler 18.0 were used. SPSS V.18 was used to analyze the linear regression and paired t-test tests, too. Results: The results showed that in the first model (SPL <70 dBA), the 8KHz frequency with the weight of 31% had the highest effect, the factors of work experience and the frequency of 250Hz each with the weight of 3%, had the least effect, and the accuracy of the model was 100%. In the second model (SPL 70–80 dBA) the frequency of 8KHz with the weight of 21% had the highest effect, the frequency of 250Hz and the working experience each had the lowest effect with the weight of 7% and the accuracy of the model was calculated as 100%. In the third model (SPL >85 dBA), the 4KHz frequency with the weight of 31% had the highest effect, and the work experience with a weight of 1% had the lowest effect, and the accuracy of the model was 94%. In the fourth model, the 4KHz frequency with the weight of 22% had the highest effect and 250Hz and age each with the weight of 8% had the lowest effects; the accuracy of this model was calculated to be 99.05%. Conclusions: During investigating and determining the weight of the factors affecting hearing loss by the C5 algorithm, the high weight and effect of the 4KHz frequency were predicted in hearing loss changes. Considering the high accuracy obtained in this modeling, this algorithm is a suitable and powerful tool for predicting and modeling the hearing loss.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Predicting and Weighting the Factors Affecting Workers’ Hearing Loss Based on Audiometric Data Using C5 Algorithm

Ghotbi-Ravandi

ElahiShirvan

Ahsaee

et al. 2019

Annals of Global Health

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“…Federation is becoming common in neuroimaging projects such as schizophrenia (Alpert, Kogan, Parrish, Marcus, & Wang, 2015) and the ENIGMA project for many neurodegeneration and neurodevelopmental diseases (Thompson et al, 2014). This is where "Big Data" analytical tools such as data mining (Ramos-Miguel, Perez-Zaballos, Perez, Falconb, & Ramosb, 2014;Tan et al, 2013) could be used to uncover potential biomarkers for positive outcomes for amplification. Combining such data will require techniques such as diffeomorphometry (Miller et al, 2014;Ratnanather et al, 2020) to map imaging data to common coordinates for analysis and comparison.…”

Section: Future Directions and Opportunitiesmentioning

confidence: 99%

Structural neuroimaging of the altered brain stemming from pediatric and adolescent hearing loss—Scientific and clinical challenges

Ratnanather

2019

WIREs Mechanisms of Disease

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There has been a spurt in structural neuroimaging studies of the effect of hearing loss on the brain. Specifically, magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) technologies provide an opportunity to quantify changes in gray and white matter structures at the macroscopic scale. To date, there have been 32 MRI and 23 DTI studies that have analyzed structural differences accruing from pre-or peri-lingual pediatric hearing loss with congenital or early onset etiology and postlingual hearing loss in pre-to-late adolescence. Additionally, there have been 15 prospective clinical structural neuroimaging studies of children and adolescents being evaluated for cochlear implants. The results of the 70 studies are summarized in two figures and three tables. Plastic changes in the brain are seen to be multifocal rather than diffuse, that is, differences are consistent across regions implicated in the hearing, speech and language networks regardless of modes of communication and amplification. Structures in that play an important role in cognition are affected to a lesser extent. A limitation of these studies is the emphasis on volumetric measures and on homogeneous groups of subjects with hearing loss.It is suggested that additional measures of morphometry and connectivity could contribute to a greater understanding of the effect of hearing loss on the brain. Then an interpretation of the observed macroscopic structural differences is given. This is followed by discussion of how structural imaging can be combined with functional imaging to provide biomarkers for longitudinal tracking of amplification.

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“…Data mining is a process, which is used to extract potentially useful information from large amounts of data (26). Usual methods of statistical analysis cannot analyze and process some information because they are too abundant and complicated, but data mining is the technique, which can change these complex data into helpful information (27).…”

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confidence: 99%

Roles of triglyceride and phosphate in atherosclerosis of diabetic hemodialysis patients

Rezapour

Payani²,

Taran

et al. 2017

Med. J. Islam. Republic Iran

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Background: A growing number of patients with End-Stage Renal Disease (ESRD) are undergoing long-term hemodialysis (HD). HD needs a vascular access (VA) and complications of VA account for a sizable proportion of its costs. One of the important cardiovascular diseases (CVD) is atherosclerosis, which is a major cause of premature deaths in the world. So, it is essential to find the risk factors to treat them before they cause an obvious CVD.Methods: We analyzed data from 174 ESRD patients who were candidate for Arterio Venous Fistula (AVF) creation from April 2008 to March 2009 in Hasheminejad Kidney Center by convenient sampling. X-ray images were used and C 4.5 algorithm of data mining techniques revealed the roles of two risk factors for atherosclerosis of diabetic ESRD patients. Pearson coefficient was also used to measure the correlation between the parameters.Results: Diabetic patients had significantly more calcified arteries in their forearm X-ray than other patients (p<0.001). Occurrence of atherosclerotic CVD in diabetic HD patients has an adverse relation compared with the controlled levels of their plasma levels of Triglyceride (TG) and Phosphorus. We found an inverse effect of TG and phosphorus plasma levels on the atherosclerotic involvement of radial and ulnar arteries in diabetic HD patients. We observed that the prevalence of radial and ulnar arteries calcification in these patients is lower when they have higher plasma levels of TG and phosphorous.Conclusion: This study investigates the role of high plasma levels of TG and phosphorous in the development of atherosclerosis in diabetic HD patients. Although many studies showed that hypertriglyceridemia plays a promoting role in the development of CVD, our study also found an inverse effect of plasma levels of TG on the atherosclerotic involvement of radial and ulnar arteries in diabetic patients, and therefore our results support this suspicion that hypertriglyceridemia plays a significant role in developing atherosclerosis.

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Use of data mining to predict significant factors and benefits of bilateral cochlear implantation

Cited by 14 publications

References 10 publications

Predicting and Weighting the Factors Affecting Workers’ Hearing Loss Based on Audiometric Data Using C5 Algorithm

Predicting and Weighting the Factors Affecting Workers’ Hearing Loss Based on Audiometric Data Using C5 Algorithm

Structural neuroimaging of the altered brain stemming from pediatric and adolescent hearing loss—Scientific and clinical challenges

Roles of triglyceride and phosphate in atherosclerosis of diabetic hemodialysis patients

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