Abstract:
"
Background: Asthma is an airway-induced inflammatory lung disease that causes
breathlessness, wheezing and regular potentially fatal attacks. Diagnosis of asthma at
affordable costs is often challenging due to its variability. A machine learning approach
and an appropriate application to provide asthma status prediction would be valuable
in clinical practice and self-detection of the disease.
Methods: This study utilized data of 596 asthmatics and 5898 non-asthmatics who
participated in the Sri Lanka Health and Ageing Survey (SLHAS) during the 2018-2019
period in Sri Lanka. Both doctor-diagnosed asthma and patient-reported asthma were
considered when deciding the asthma status of a patient. In this research, thirteen
machine learning classification algorithms were built on under-sampled data, and ten
algorithms were created using imbalanced data. These include machine learning models
such as; Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random
Forest, Naïve Bayes, K-Nearest Neighbors (KNN), Gradient Boost, XGBoost,
AdaBoost, CatBoost, LightGBM, Multi-Layer Perceptron (MLP), and Probabilistic
Neural Network (PNN). The performances of these algorithms were evaluated by
employing various measures, including Area Under Curve of Receiver Operating
Characteristics (AUC ROC) and confusion matrix related indices.
Results: The model comparison showed that a Hybrid version of Logistic Regression
and LightGBM obtained the highest model performance with AUC and sensitivity of
0.9062 and 79.85%. The developed Hybrid models take wheeze related parameters,
Shortness of Breath (SOB) attacks, coughing attacks, tightness in the chest, nasal
allergies, physical activeness, exposure to passive smoking, ethnicity and sector, as
input parameters and predicts the asthma status. The web application developed eases
the burden of users by allowing them to get their own estimates upon entering data.
Conclusion: A combination of Logistic Regression and LightGBM models can be
utilized to predict the presence of adult asthma successfully. The proposed expert
system helps patients in their diagnosis of asthma in both self-diagnosis and clinical
diagnosis aspects."
