Early Diagnosis of Hypothyroidism in the Eastern Province of Saudi Arabia Using Computational Intelligence Techniques

Summary

This study investigates four machine learning algorithms: K-nearest neighbors (KNN), support vector machine (SVM), extreme gradient boosting (XGBoost), and a soft voting ensemble classifier in the proactive diagnosis of hypothyroidism. The algorithms were shortlisted based on a critical literature review and employed over a locally collected dataset from Saudi Arabia.  

Abstract

Hypothyroidism is one of the most common yet underdiagnosed medical conditions in Saudi Arabia. It is more prevalent in aged and pregnant women as well as in patients with diabetes and sleep apnea. Hypothyroidism is characterized by the thyroid gland producing inadequate thyroid hormones, which might result in other chronic illnesses if left untreated. For this reason, this study proposes machine learning techniques to preemptively diagnose this disease using a straightforward clinical dataset from Saudi Arabia. Given the data size, this work serves as proof of concept. Algorithms such as KNN, SVM, Gradient boosting, and soft voting ensemble classifier were chosen for their promising performance in the proactive diagnosis of hypothyroidism and associated diseases compared to other algorithms in literature. The best performing model was the soft voting ensemble classifier, which achieved an accuracy of 94.7%. SVM, KNN, and XGBoost achieved 94%, 93.42%, and 92.1% accuracies, respectively. These results were obtained using 10 fold cross validation and forward sequential feature selection.

Introduction

Thyroid dysfunction (TD) is one of the most common chronic endocrine illnesses, with varying prevalence across populations¹. The two most common thyroid gland disorders are hypothyroidism and hyperthyroidism. Hypothyroidism is a prevalent condition that results from insufficient thyroid hormone production. While it is generally manageable with treatment, in severe instances, it can be fatal if not addressed. The most common symptoms of hypothyroidism in adults include fatigue, constipation, lethargy, weight gain, dry skin, and voice changes. However, clinical presentations may vary by age, gender, and other factors³. It ....

Protocol

1. Methodology

NOTE: As described in Table of Materials, the experiment for this research was conducted in Python on Jupyter Notebook, which was used to develop machine learning models for preemptive diagnosis of Hypothyroidism. Microsoft Excel and the Python Scikit-Learn library were used in the preprocessing stage. K-nearest neighbor (KNN), support vector machine (SVM), extreme gradient boosting (XGBoost), and a soft voting ensemble classifier composed of KNN and SVM were then used to train the dataset, with GridSearchCV and 10-fold cross-validation to obtain optimal hyperparameters. Additionally, the featu....

Results

Performance of models
The performance of the four models created in this study was compared and analyzed regarding their accuracy, recall, and testing. GridsearchCV algorithm was utilized to identify the best hyperparameter for each model using all features. The performance of each model was evaluated with a stratified 10 fold cross validation. Table 5 presents the results of each model’s performance.

Discussion

In this study, machine learning algorithms on a Saudi clinical dataset are investigated to develop a preemptive diagnostic model for hypothyroidism. Four models were created: KNN, SVM, XGBoost, and Soft Voting Ensemble Classifier. All four models identified age and respiratory rate as significant indicators in the preemptive diagnosis of the disease. Among the four models, the Voting Classifier demonstrated superior performance across all metrics used in this study. Utilizing just five features, it achieved an accuracy o.......

Materials

List of materials used in this article

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