Impact of Ethnicity on Prevalance of Lung Diseases

Abstract

Lung disorder is a major public health concern worldwide, and it has?been suggested that the ethnic origins may affect the incidence of such diseases. This study explores the influence of ethnicity on lung disease prevalence in a manner accounting for genetic,socio-economicand the environmentalrisks. Epidemiology — the study of how often diseases occur in different groups of people and whypopulation-based health care databases are scrutinized to examine differences in rates of lung?disorder by ethnicity. Potentially leading to?disparities in lung health phenotypes that are influenced by genetic risk factors and environmental exposures. Such differentials are crucially relevant for designing and implementing effective, population-appropriate?interventions.

Introduction

Asthma, COPD, and lung cancer are major respiratory diseases contributing significantly to global morbidity and mortality. The burden of these diseases varies among populations due to a mix of genetic, socio-economic, and environmental factors. Ethnic minorities, such as African-Americans and Hispanics, tend to have higher susceptibility, influenced by genetics, healthcare access, lifestyle, and environmental exposure, particularly air pollution and smoking.

The study proposes a machine learning (ML) model—specifically a Naive Bayes classifier—that integrates multiple variables like ethnicity, average Air Quality Index (AQI), and smoking prevalence to predict lung disease risk more accurately than previous models that considered these factors separately. This approach helps identify high-risk populations and supports targeted interventions such as improving air quality and smoking cessation programs, thus advancing precision medicine and health equity.

Prior research confirms ethnic disparities in respiratory disease linked to environmental and lifestyle factors, but often studies focus on single parameters. This work bridges that gap by combining demographic and environmental data using ML to generate actionable insights for healthcare providers and policymakers.

Using a synthesized dataset of 1,000 records with evenly distributed ethnic groups, the Naive Bayes model achieved 80% accuracy in classifying lung disease risk. Results highlighted African-Americans as highest risk, correlating with high AQI and smoking rates, while Asians generally showed lower risk.

Limitations include the synthetic nature of the data and lack of genetic and individual health markers. Policy recommendations include enhancing air quality monitoring, targeted anti-smoking campaigns, and improving healthcare access in vulnerable communities.

Future work suggests incorporating clinical data, time series analysis, GIS for spatial risk mapping, and exploring other ML algorithms and deep learning methods to improve predictive accuracy and utility.

Conclusion

This study highlights the vast potential of machine learning models in addressing public health challenges, in this case, in forecasting and monitoring respiratory disease risk.The use of machine learning on healthcare systems provides a platform of risk identification and intervention targeting which are essential towards minimizing the risk of respiratory disease worldwide. For example, clinical health workers can use such models for the detection of high-risk communities, efficient allocation of resources, and the prescription of personalized prevention interventions. Policy makers, on the other hand, can use such data towards the implementation of evidence-based interventions, such as stricter air-quality regulations and anti-smoking measures among vulnerable populations. Moreover, the system has been 80% correct, testifying to its value as a predictor of risk for lung disease. Irrespective of application of artificial data, the study illustrates the ability of machine learning to deal with many variables and deliver results that are useful. By combining demographic, environmental, and lifestyle variables, the model transcends the traditional single-variable analysis, offering a more complete risk assessment model. In the near future, extension of this system can greatly improve its utility and reach.Geolocation-based automation of AQI and smoker population proportion\'s data pull would make the process less cumbersome and the system usability and scalability improve. Additionally, incorporation of this tool with hospital management software, public health dashboard, or mobile health app will further enhance its use, and convenient and accurate information on lung disease risk to patients and practitioners would be presented. Since the machine learning algorithm improves, further optimization can then be performed with more complex models such as Random Forest, Gradient Boosting, or Deep Learning algorithms. More complex models have the capability of handling more complicated and bigger data, therefore increasing the accuracy of the prediction and unlocking more deeper patterns of health disparities. Through the identification and resolution of ethnic and environmental health inequities, the system promotes global health equity and sustainable development goals. Finally, this tool would be a useful addition to the prevention of the development of lung disease, enhancing the health of the population, and making the world a healthier place for the world\'s population.

References

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Copyright

Copyright © 2025 Alok Singh, Dhruv Gupta, Devansh Verma, Dr. Yogendra Pal. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.