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Open AccessArticle
Tongue Disease Prediction Based on Machine Learning Algorithms
by
Ali Raad Hassoon
Ali Raad Hassoon 1,2,
Ali Al-Naji
Ali Al-Naji 1,*
,
Ghaidaa A. Khalid
Ghaidaa A. Khalid 1
and
Javaan Chahl
Javaan Chahl 3
1
Electrical Engineering Technical College, Middle Technical University, Baghdad 10022, Iraq
2
Al Hussein Teaching Hospital, Nasiriyah 64001, Iraq
3
School of Engineering, University of South Australia, Mawson Lakes, SA 5095, Australia
*
Author to whom correspondence should be addressed.
Technologies 2024, 12(7), 97; https://doi.org/10.3390/technologies12070097 (registering DOI)
Submission received: 5 April 2024
/
Revised: 6 June 2024
/
Accepted: 21 June 2024
/
Published: 28 June 2024
Abstract
The diagnosis of tongue disease is based on the observation of various tongue characteristics, including color, shape, texture, and moisture, which indicate the patient’s health status. Tongue color is one such characteristic that plays a vital function in identifying diseases and the levels of progression of the ailment. With the development of computer vision systems, especially in the field of artificial intelligence, there has been important progress in acquiring, processing, and classifying tongue images. This study proposes a new imaging system to analyze and extract tongue color features at different color saturations and under different light conditions from five color space models (RGB, YcbCr, HSV, LAB, and YIQ). The proposed imaging system trained 5260 images classified with seven classes (red, yellow, green, blue, gray, white, and pink) using six machine learning algorithms, namely, the naïve Bayes (NB), support vector machine (SVM), k-nearest neighbors (KNN), decision trees (DTs), random forest (RF), and Extreme Gradient Boost (XGBoost) methods, to predict tongue color under any lighting conditions. The obtained results from the machine learning algorithms illustrated that XGBoost had the highest accuracy at 98.71%, while the NB algorithm had the lowest accuracy, with 91.43%. Based on these obtained results, the XGBoost algorithm was chosen as the classifier of the proposed imaging system and linked with a graphical user interface to predict tongue color and its related diseases in real time. Thus, this proposed imaging system opens the door for expanded tongue diagnosis within future point-of-care health systems.
Share and Cite
MDPI and ACS Style
Hassoon, A.R.; Al-Naji, A.; Khalid, G.A.; Chahl, J.
Tongue Disease Prediction Based on Machine Learning Algorithms. Technologies 2024, 12, 97.
https://doi.org/10.3390/technologies12070097
AMA Style
Hassoon AR, Al-Naji A, Khalid GA, Chahl J.
Tongue Disease Prediction Based on Machine Learning Algorithms. Technologies. 2024; 12(7):97.
https://doi.org/10.3390/technologies12070097
Chicago/Turabian Style
Hassoon, Ali Raad, Ali Al-Naji, Ghaidaa A. Khalid, and Javaan Chahl.
2024. "Tongue Disease Prediction Based on Machine Learning Algorithms" Technologies 12, no. 7: 97.
https://doi.org/10.3390/technologies12070097
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