Tongue Disease Prediction Based on Machine Learning Algorithms

• Published in: Technologies (Basel) Vol. 12; no. 7; p. 97
• Main Authors: Hassoon, Ali Raad, Al-Naji, Ali, Khalid, Ghaidaa A, Chahl, Javaan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-07-2024
• Subjects: Accuracy; Algorithms; Artificial intelligence; artificial intelligence techniques; Chinese medicine; Color; Computer vision; computer vision systems; COVID-19; Data mining; Decision trees; Deep learning; Development and progression; Diabetes; Diagnosis; Digital cameras; Disease; Diseases; Graphical user interface; Hyperglycemia; Image acquisition; Infections; Iraq; Machine learning; Machine vision; Medical imaging; Medical research; Medicine, Experimental; Neural networks; Smartphones; Support vector machines; Tongue; tongue color analysis; traditional Chinese medicine; Vision systems; Iraq
• ISSN: 2227-7080; 2227-7080
• DOI: 10.3390/technologies12070097

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.