Machine Learning Models for Predicting the Occurrence of Respiratory Diseases Using Climatic and Air-Pollution Factors

Ku, Yun Hyi; Kwon, Soon Bin; Yoon, Jeong-Hwa; Mun, Seog‐Kyun; Chang, Mun Seog

doi:10.21053/ceo.2021.01536

Cited by 13 publications

(6 citation statements)

References 38 publications

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“…Our algorithm achieved an R2 value of 99%, outperforming the approach proposed by Khatri K L, Tamil L S (2) and Khaiwal R, Bahadur S S, Katoch V, Bhardwaj S, Kaur-Sidhu M, Gupta M, et al (4) by a margin of 17% and 12% respectively. Also, our algorithm demonstrated higher R2 and RMSE values, with R2 reaching 99% and RMSE 0.46 and 0.22, compared to the R2 of 67% and RMSE of 13.9 reported by Ku Y, Kwon SB, Yoon JH, Mun SK, and Chang M (3) . In addition to the above findings, our algorithm demonstrates remarkably low forecasting errors for ARI and Pneumonia, with rates of 2.25% and 5.12% respectively.…”

Section: Resultscontrasting

confidence: 43%

“…Moreover, the research did not take into account separating the meteorological and air pollution data individually. Ku Y, Kwon SB, Yoon JH, Mun SK, and Chang M (3) compare Gaussian process regressor and gradient boosting methods for patient arrival forecasting for the period of 2014-2019. Both models demonstrated competitive prediction performance, with R2 values of over 0.67 and RMSE values below 13.9.…”

Section: Introductionmentioning

confidence: 99%

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Multioutput Ensemble Machine Learning Algorithm: A Prediction Model of Acute Respiratory infection and Pneumonia Occurrence

Pimpale,

Pandit

2023

IJST

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Objectives: To forecast daily OPD patients based on air pollution and weather parameters, the objective is to build a robust model that accurately predicts patient volume by considering major missing values and factors such as PM2.5 levels, temperature, humidity, wind speed, and rainfall, etc. thereby improving healthcare planning and delivery. Methods: To develop the multioutput ensemble model for forecasting daily OPD (out-patient department), we have used 13 machine learning techniques such as regression analysis, Extra tree regressor, Support vector regressor, etc. We have collected and pre-processed data from multiple sources, including air quality and weather parameters from NASA's website, and historical healthcare data from Shatabdi Hospital, Govandi, Mumbai. We have developed the model using a combination of Gaussian regressor and Extra tree regressor and evaluated its performance using metrics such as FastDTW, RMSE, etc. Findings: The prediction result shows that the multioutput ensemble model performed significantly better than other models even with the presence of outliers, multicollinearity, and non-stationarity with Root Mean Squared Error 0.46 and 0.22 for ARI and Pneumonia with lag 7 days and 8 days respectively. Moreover, this model also worked well including Covid-19 period data when there was a negligible correlation between independent and dependent variables. Novelty: None of the datasets that have been used for the prediction of time series data have had a significant gap in recorded data in the time domain which has been effectively taken care of in this research. Secondly, all the earlier research work in this domain addresses only a single disease that provides the same lag value irrespective of the disease. The period of expression after the event occurrence may vary for multiple diseases, albeit in one domain that is triggered by similar and /or different air pollutants. This issue has been addressed by ensembling multiple ML algorithms to effectively optimize time complexity.

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Section: Resultscontrasting

confidence: 43%

Section: Introductionmentioning

confidence: 99%

Multioutput Ensemble Machine Learning Algorithm: A Prediction Model of Acute Respiratory infection and Pneumonia Occurrence

Pimpale,

Pandit

2023

IJST

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“…More associations will be identified with the accumulation of NHIRD and EPA monitoring data. ML models for predicting the incidence of disease using environmental and air pollution factors could evolve into medical and public health warning systems 67 .…”

Section: Discussionmentioning

confidence: 99%

Long-term exposure to particulate matter was associated with increased dementia risk using both traditional approaches and novel machine learning methods

Yan

Chen

Yang

et al. 2022

Sci Rep

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Air pollution exposure has been linked to various diseases, including dementia. However, a novel method for investigating the associations between air pollution exposure and disease is lacking. The objective of this study was to investigate whether long-term exposure to ambient particulate air pollution increases dementia risk using both the traditional Cox model approach and a novel machine learning (ML) with random forest (RF) method. We used health data from a national population-based cohort in Taiwan from 2000 to 2017. We collected the following ambient air pollution data from the Taiwan Environmental Protection Administration (EPA): fine particulate matter (PM2.5) and gaseous pollutants, including sulfur dioxide (SO2), carbon monoxide (CO), ozone (O3), nitrogen oxide (NOx), nitric oxide (NO), and nitrogen dioxide (NO2). Spatiotemporal-estimated air quality data calculated based on a geostatistical approach, namely, the Bayesian maximum entropy method, were collected. Each subject's residential county and township were reviewed monthly and linked to air quality data based on the corresponding township and month of the year for each subject. The Cox model approach and the ML with RF method were used. Increasing the concentration of PM2.5 by one interquartile range (IQR) increased the risk of dementia by approximately 5% (HR = 1.05 with 95% CI = 1.04–1.05). The comparison of the performance of the extended Cox model approach with the RF method showed that the prediction accuracy was approximately 0.7 by the RF method, but the AUC was lower than that of the Cox model approach. This national cohort study over an 18-year period provides supporting evidence that long-term particulate air pollution exposure is associated with increased dementia risk in Taiwan. The ML with RF method appears to be an acceptable approach for exploring associations between air pollutant exposure and disease.

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“…We could not include the treatment histories of medication and surgical procedures for CP and DNI, which may have led to heterogeneity in the analyses. Although we included several variables, including demographic and socioeconomic factors and comorbidities, there are several potential confounders, such as a history of tonsillectomy or other neck surgeries and a history of airway infection, that can influence the relationship between CP and DNI [29,30]. Further studies may be warranted to determine the specific associations between CP and DNI according to severity and subtype.…”

Section: Discussionmentioning

confidence: 99%

Associations between Peritonsillar Abscess and Deep Neck Infection in Chronic Periodontitis Patients: Two Nested Case—Control Studies Using a National Health Screening Cohort

Kim,

Park,

Byun

et al. 2024

JCM

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Background/Introduction: Odontogenic infection is one of the main etiologies of deep neck infection (DNI). However, the relationship between chronic periodontitis (CP) and the incidence of DNI has not been examined. This study aimed to evaluate the incidence of DNI and peritonsillar abscess (PTA) after CP. Methods: The Korean National Health Insurance Service-National Sample Cohort 2002–2019 was used. In Study I, 4585 PTA patients were matched with 19,340 control I participants. A previous history of CP for 1 year was collected, and the odds ratios (ORs) of CP for PTA were analyzed using conditional logistic regression. In Study II, 46,293 DNI patients and 185,172 control II participants were matched. A previous history of CP for 1 year was collected, and conditional logistic regression was conducted for the ORs of CP for DNI. Secondary analyses were conducted in demographic, socioeconomic, and comorbidity subgroups. Results: In Study I, a history of CP was not related to the incidence of PTA (adjusted OR = 1.28, 95% confidence interval [CI] = 0.91–1.81). In Study II, the incidence of DNI was greater in participants with a history of CP (adjusted OR = 1.55, 95% CI = 1.41–1.71). The relationship between CP history and DNI was greater in groups with young, male, low-income, and rural residents. Conclusions: A prior history of CP was associated with a high incidence of DNI in the general population of Korea. Patients with CP need to be managed for the potential risk of DNI.

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Machine Learning Models for Predicting the Occurrence of Respiratory Diseases Using Climatic and Air-Pollution Factors

Cited by 13 publications

References 38 publications

Multioutput Ensemble Machine Learning Algorithm: A Prediction Model of Acute Respiratory infection and Pneumonia Occurrence

Multioutput Ensemble Machine Learning Algorithm: A Prediction Model of Acute Respiratory infection and Pneumonia Occurrence

Long-term exposure to particulate matter was associated with increased dementia risk using both traditional approaches and novel machine learning methods

Associations between Peritonsillar Abscess and Deep Neck Infection in Chronic Periodontitis Patients: Two Nested Case—Control Studies Using a National Health Screening Cohort

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