Machine learning approaches for asthma disease prediction among adults in Sri Lanka

ishwari — Wed, 18 Sep 2024 09:53:33 +0000

Machine learning approaches for asthma disease prediction among adults in Sri Lanka ishwari Wed, 09/18/2024 - 05:53

Abstract

Objectives: Addressing the challenge of cost-effective asthma diagnosis amidst diverse symptom patterns among patients, this study aims to develop a machine learning-based asthma prediction tool for self-detection of asthma. Methods: Data from 6,665 participants in the Sri Lanka Health and Ageing Study (2018-2019) are used for this research. Thirteen machine learning algorithms, including Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, Na¨ıve Bayes, K-Nearest Neighbors, Gradient Boost, XGBoost, AdaBoost, CatBoost, LightGBM, Multi-Layer Perceptron, and Probabilistic Neural Network, are employed. Results: A hybrid version of Logistic Regression and LightGBM outperformed other models, achieving an AUC of 0.9062 and 79.85% sensitivity. Key predictive features for asthma include wheezing, breathlessness with wheezing, shortness of breath attacks, coughing attacks, chest tightness, nasal allergies, physical activity, passive smoking, ethnicity, and residential sector. Conclusion: Combining Logistic Regression and LightGBM models can effectively predict adult asthma based on self-reported symptoms and demographic and behavioural characteristics. The proposed expert system assists clinicians and patients in diagnosing potential asthma cases.

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Gunawardana, J. R. N. A., Viswakula, S. D., Rannan-Eliya, R. P., & Wijemunige, N. (2024). Machine learning approaches for asthma disease prediction among adults in Sri Lanka. Health Informatics Journal, 30(3), 14604582241283968. https://doi.org/10.1177/14604582241283968

This article is available under the Creative Commons CC-BY-NC license.

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Wed, 09/18/2024 - 12:00

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Non-communicable Diseases

Increased Intensity Of PCR Testing Reduced COVID-19 Transmission Within Countries During The First Pandemic Wave

sachini — Wed, 03 Jul 2024 06:06:26 +0000

Increased Intensity Of PCR Testing Reduced COVID-19 Transmission Within Countries During The First Pandemic Wave sachini Wed, 07/03/2024 - 02:06

Abstract

Experts agree that reverse transcription–polymerase chain reaction (PCR) testing is critical in controlling coronavirus disease 2019(COVID-19), but decision makers disagree on how much testing is optimal. Controlling for interventions and ecological factors, we used linear regression to quantify testing's impact on COVID-19's average reproduction number, which represents transmissibility, in 173 countries and territories (which account for 99 percent of the world's COVID-19cases) during March–June 2020. Among interventions, PCR testing had the greatest influence: a tenfold increase in the ratio of tests to new cases reported reduced the average reproduction number by 9 percent across a range of testing levels. Our results imply that mobility reductions (for example, shelter-in-place orders) were less effective in developing countries than in developed countries. Our results help explain how some nations achieved near-elimination of COVID-19 and the failure of lockdowns to slow COVID-19 in others. Our findings suggest that the testing benchmarks used by the World Health Organization and other entities are insufficient for COVID-19 control. Increased testing and isolation may represent the most effective, least costly alternative in terms of money, economic growth, and human life for controllingCOVID-19.

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Ravi P. Rannan-Eliya

Nilmini Wijemunige

Nishani Gunawardana

Sarasi N. Amarasinghe

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Increasing PCR testing reduced COVID-19 cases and deaths, and it was the most important predictor of how well countries contained the pandemic. The study also found that lockdowns did not slow the virus in most countries, and that masks and school closures had less impact than high levels of testing and isolation.

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