A Computational Intelligence-based Early Diagnosis of Asthma Disease: A Saudi Arabian Case Study

Summary

The current study investigates several machine learning algorithms on a Saudi Arabian dataset for asthma detection. In contrast to state-of-the-art approaches that employ machine learning on clinical datasets for asthma diagnostics, the proposed scheme achieves better performance, with a 3.9% improvement in diagnosis accuracy.

Abstract

According to research, an increase in chronic diseases, including asthma, has been identified. The number of asthma patients in Saudi Arabia is a cause for concern due to the weather conditions and lifestyle, especially in the post-pandemic era. It demands a solution to reduce infections by developing an intelligent system to detect asthma at an early stage, thereby preventing the disease or enabling early treatment. In this study, machine learning has been utilized to develop tools to track asthma symptoms at an early stage. Although there have been various prior attempts to apply machine learning to predict the occurrence of asthma. Nevertheless, focusing on the identification of the disease at the pre-symptom stage, particularly in the Saudi Arabian context, is a relatively neglected area. The dataset for the current study was obtained from King Fahad University Hospital, Dammam, Saudi Arabia, and included standard tests performed on patients, including blood tests, viral tests, and biochemistry tests. The dataset contains 17 significant attributes and includes information for 328 asthma patients: 165 are positive, and 163 are negative. The methods selected for application here are random forests (RF), artificial neural networks (ANN), support vector machines (SVM), and naive Bayes (NB). Each of these methods has been chosen based on its distinctive features. The experimental outcome revealed that the RF, SVM, and ANN approaches yielded 94%, which is the highest accuracy and improved upon the state of the art by 3.9%. It is worth noting that nine of the seventeen possible features were used to achieve the above accuracy. Despite RF, SVM, and ANN achieving the same accuracy, ANN has a higher error cost. Therefore, RF and SVM are superior based on the pattern of results, and hence they are suggested for this problem.

Introduction

After conducting extensive research, the researchers observed that chronic diseases have become increasingly common¹. Asthma is a chronic inflammatory disease of the airways characterized by recurrent episodes of breathlessness and wheezing. The prevalence of asthma varies globally, ranging from 1–20% for both children and adults, affecting millions of people worldwide². These large-scale changes are related to environmental variations, including those in various countries, as well as the use of different assessment instruments and diverse epidemiologic definitions of asthma. Asthma has a relatively low fatality ra....

Protocol

This section describes the machine learning algorithms investigated in the proposed asthma diagnostics approach. The criteria and reasons behind the selection of the proposed method are based on a comprehensive literature review and a long history of dealing with preemptive diagnosis of several chronic diseases^{27,28,29,30,31}. These algorithms produced promising results. All the materials and tools used in this study are listed in the Table of Materials.

S....

Results

Influence of feature selection
In the current study, the recursive feature elimination-based correlation coefficient method is used for the feature selection. The correlation coefficient is used to order the features from the highest to the lowest according to their correlation values. Recursive feature elimination is used to assist in selecting the suitable features for the model, as it progressively eliminates the weakest features until only one remains. Every process runs the four selected classif.......

Discussion

The current study employs several machine learning algorithms, including SVM, ANN, RF, and NB. Upon conducting several experiments, fine-tuning hyperparameters and feature selection, it is concluded that SVM, ANN, and RF exhibit a remarkable diagnostic accuracy of 94%, while NB is comparatively lower at 83.33%. The results were validated using 10-fold cross-validation and optimized feature selection, as well as the best split ratio. According to the given data, seventeen parameters of the patients were ranked in order of.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Excel 365	Microsoft	Excel 365	Used to store raw data in csv format
Laptop/Machine	Dell	XPS9320	RAM 16GB, 12th Gen Intel(R) Core(TM) i7-1260P
Mlxtend 0.23.4	Google colab Notebook	Mlxtend 0.23.4	Used for model building training
Numpy 2.0.2	Google colab Notebook	Numpy 2.0.2	Used for model building training
Pandas 2.2.2	Google colab Notebook	Pandas 2.2.2	Used for model building training
Python 3.12.12	Google colab Notebook	Python 3.12.12	Used for model building training
Sklearn 1.6.1	Google colab Notebook	Sklearn 1.6.1	Used for model building training
SPSS	IBM, USA	Version 26.0	Used for statisitcal analysis
XGbbost 3.1.2	Google colab Notebook	XGbbost 3.1.2	Used for model building training