Analyzing Audiometric Data of Agricultural Tractor Drivers in India Using Data Mining Techniques

Area of Science:

• Occupational Health
• Audiology
• Data Mining

Background:

• Agricultural tractor noise is a major occupational hazard leading to Noise Induced Hearing Loss (NIHL).
• Indian Agricultural Tractor Drivers (ATD) are at risk, necessitating risk assessment and understanding of hearing impairment patterns.

Purpose of the Study:

• To assess NIHL risk in Indian ATD.
• To apply unsupervised data mining (k-means clustering) for classifying audiometric data and understanding hearing impairment.

Main Methods:

• Cross-sectional study of audiometric profiles of Indian ATD (5-43 years experience).
• Audiometry testing across frequencies (0.125 kHz to 8 kHz).
• K-means clustering applied to audiometric data; ANOVA used to assess factor significance (age, experience, weight).

Main Results:

• Hearing threshold levels increased with over 10 years of experience.
• K-means clustering revealed statistically significant differences across audiometric frequencies and between clusters.
• Prolonged tractor operation significantly impacted hearing in both ears.

Conclusions:

• K-means clustering is effective for analyzing audiometric data in occupational noise exposure studies.
• Findings support the development of targeted hearing conservation programs for agricultural workers.
• Recommendations include legislative awareness and promoting quieter tractor designs.