Tongue Disease Prediction Based on Machine Learning Algorithms
Abstract
:1. Introduction
2. Related Work
3. Materials and Methods
3.1. Data Collection
3.2. Experimental Configuration
3.3. System Design
3.3.1. Image Analysis
3.3.2. Tongue Color as a Diagnostic Indicator for Diseases
3.3.3. MATLAB App Designer
3.4. Implementation of Machine Learning Algorithms as Classifiers
3.4.1. Naïve Bayes Categorizer
- The model leverages polynomial or binary data.
- The approach permits the application of different datasets.
- The method avoids matrix computations, mathematical optimization, etc., so its application is comparatively straightforward.
3.4.2. Support Vector Machine (SVM)
3.4.3. K-Nearest Neighbors Classification (KNN)
- Input: The collection of training rows and the unlabeled test picture.
- Procedure: The proximity between the uncategorized test picture and every training image is calculated. The collection of nearest training images (k-nearest neighbors) to the uncategorized row is chosen.
- Output: A label is assigned to the test row based on the predominant class among its closest neighbors.
- The choice: The choice of the k value is crucial in the KNN classifier.
3.4.4. Decision Tree (DT) Classification
3.4.5. Random Forest (RF)
3.4.6. Extreme Gradient Boost (XGBoost)
3.5. Evaluation Metrics
4. Experimental Results and Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Velanovich, V. Correlations with “Non-Western” Medical Theory and Practice: Traditional Chinese Medicine, Traditional Indigenous Medicine, Faith Healing, and Homeopathy. In Medical Persuasion: Understanding the Impact on Medical Argumentation; Velanovich, V., Ed.; Springer International Publishing: Cham, Switzerland, 2023; pp. 271–306. [Google Scholar]
- Wiseman, N. Chinese medical dictionaries: A guarantee for better quality literature. Clin. Acupunct. Orient. Med. 2001, 2, 90–98. [Google Scholar] [CrossRef]
- Kirschbaum, B. Atlas of Chinese Tongue Diagnosis; Eastland Press: Seattle, WA, USA, 2000. [Google Scholar]
- Li, J.; Huang, J.; Jiang, T.; Tu, L.; Cui, L.; Cui, J.; Ma, X.; Yao, X.; Shi, Y.; Wang, S.; et al. A multi-step approach for tongue image classification in patients with diabetes. Comput. Biol. Med. 2022, 149, 105935. [Google Scholar] [CrossRef] [PubMed]
- Abdullah, A.K.; Mohammed, S.L.; Al-Naji, A. Tongue Color Analysis and Disease Diagnosis Based on a Computer Vision System. In Proceedings of the 2022 4th International Conference on Advanced Science and Engineering (ICOASE), Zakho, Iraq, 21–22 September 2022; pp. 25–30. [Google Scholar]
- Zhang, N.; Jiang, Z.; Li, J.; Zhang, D. Multiple color representation and fusion for diabetes mellitus diagnosis based on back tongue images. Comput. Biol. Med. 2023, 155, 106652. [Google Scholar] [CrossRef] [PubMed]
- Han, S.; Yang, X.; Qi, Q.; Pan, Y.; Chen, Y.; Shen, J.; Liao, H.; Ji, Z. Potential screening and early diagnosis method for cancer: Tongue diagnosis. Int. J. Oncol. 2016, 48, 2257–2264. [Google Scholar] [CrossRef] [PubMed]
- Xie, J.; Jing, C.; Zhang, Z.; Xu, J.; Duan, Y.; Xu, D. Digital tongue image analyses for health assessment. Med. Rev. 2021, 1, 172–198. [Google Scholar] [CrossRef]
- Jiang, B.; Liang, X.; Chen, Y.; Ma, T.; Liu, L.; Li, J.; Jiang, R.; Chen, T.; Zhang, X.; Li, S. Integrating next-generation sequencing and traditional tongue diagnosis to determine tongue coating microbiome. Sci. Rep. 2012, 2, 936. [Google Scholar] [CrossRef] [PubMed]
- Srividhya, E.; Muthukumaravel, A. Diagnosis of Diabetes by Tongue Analysis. In Proceedings of the 2019 1st International Conference on Advances in Information Technology (ICAIT), Chikmagalur, India, 25–27 July 2019; pp. 217–222. [Google Scholar]
- Kanawong, R.; Obafemi-Ajayi, T.; Ma, T.; Xu, D.; Li, S.; Duan, Y. Automated Tongue Feature Extraction for ZHENG Classification in Traditional Chinese Medicine. Evid.-Based Complement. Altern. Med. 2012, 2012, 912852. [Google Scholar] [CrossRef] [PubMed]
- Zhuang, Q.; Gan, S.; Zhang, L. Human-computer interaction based health diagnostics using ResNet34 for tongue image classification. Comput. Methods Programs Biomed. 2022, 226, 107096. [Google Scholar] [CrossRef] [PubMed]
- Horzov, L.; Goncharuk-Khomyn, M.; Hema-Bahyna, N.; Yurzhenko, A.; Melnyk, V. Analysis of Tongue Color-Associated Features among Patients with PCR-Confirmed COVID-19 Infection in Ukraine. Pesqui. Bras. Odontopediatria Clínica Integr. 2021, 21, e0011. [Google Scholar] [CrossRef]
- Park, Y.-J.; Lee, J.-M.; Yoo, S.-Y.; Park, Y.-B. Reliability and validity of tongue color analysis in the prediction of symptom patterns in terms of East Asian Medicine. J. Tradit. Chin. Med. 2016, 36, 165–172. [Google Scholar] [CrossRef]
- Umadevi, G.; Malathy, V.; Anand, M. Diagnosis of Diabetes from Tongue Image Using Versatile Tooth-Marked Region Classification. TEST Eng. Manag. 2019, 81, 5953–5965. [Google Scholar]
- Srividhya, E.; Muthukumaravel, A. Feature Extraction of Tongue Diseases Diagnosis Using SVM Classifier. In Proceedings of the 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), Dubai, United Arab Emirates, 11–12 December 2019; pp. 260–263. [Google Scholar]
- Thirunavukkarasu, U.; Umapathy, S.; Krishnan, P.T.; Janardanan, K. Human Tongue Thermography Could Be a Prognostic Tool for Prescreening the Type II Diabetes Mellitus. Evid.-Based Complement. Altern. Med. 2020, 2020, 3186208. [Google Scholar] [CrossRef] [PubMed]
- Naveed, S. Early Diabetes Discovery From Tongue Images. Comput. J. 2022, 65, 237–250. [Google Scholar] [CrossRef]
- Mansour, R.F.; Althobaiti, M.M.; Ashour, A.A. Internet of Things and Synergic Deep Learning Based Biomedical Tongue Color Image Analysis for Disease Diagnosis and Classification. IEEE Access 2021, 9, 94769–94779. [Google Scholar] [CrossRef]
- Abdullah, A.K.; Mohammed, S.L.; Al-Naji, A. Computer-aided diseases diagnosis system based on tongue color analysis: A review. In AIP Conference Proceedings; AIP Publishing: Long Island, NY, USA, 2023; Volume 2804, no. 1. [Google Scholar]
- Susanto, A.; Dewantoro, Z.H.; Sari, C.A.; Setiadi, D.R.I.M.; Rachmawanto, E.H.; Mulyono, I.U.W. Shallot Quality Classification using HSV Color Models and Size Identification based on Naive Bayes Classifier Shallot Quality Classification using HSV Color Models and Size Identification based on Naive Bayes Classifier. J. Phys. Conf. Ser. 2020, 1577, 012020. [Google Scholar] [CrossRef]
- Zhang, N.; Wu, L.; Yang, J.; Guan, Y. Naive Bayes Bearing Fault Diagnosis Based on Enhanced Independence of Data. Sensors 2018, 18, 463. [Google Scholar] [CrossRef] [PubMed]
- Goswami, M.; Sebastian, N.J. Performance Analysis of Logistic Regression, KNN, SVM, Naïve Bayes Classifier for Healthcare Application During COVID-19. In Innovative Data Communication Technologies and Application; Springer Nature: Singapore, 2022; pp. 645–658. [Google Scholar]
- Jun, Z. The Development and Application of Support Vector Machine. J. Phys. Conf. Ser. 2021, 1748, 052006. [Google Scholar] [CrossRef]
- Sarker, I.H. Machine Learning for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects. Ann. Data Sci. 2023, 10, 1473–1498. [Google Scholar] [CrossRef]
- Bhavani, R.R.; Jiji, G.W. Image registration for varicose ulcer classification using KNN classifier. Int. J. Comput. Appl. 2018, 40, 88–97. [Google Scholar] [CrossRef]
- Wang, A.X.; Chukova, S.S.; Nguyen, B.P. Ensemble k-nearest neighbors based on centroid displacement. Inf. Sci. 2023, 629, 313–323. [Google Scholar] [CrossRef]
- Zafra, A.; Gibaja, E. Nearest neighbor-based approaches for multi-instance multi-label classification. Expert. Syst. Appl. 2023, 232, 120876. [Google Scholar] [CrossRef]
- Villarreal-Hernández, J.Á.; Morales-Rodríguez, M.L.; Rangel-Valdez, N.; Gómez-Santillán, C. Reusability Analysis of K-Nearest Neighbors Variants for Classification Models. In Innovations in Machine and Deep Learning: Case Studies and Applications; Rivera, G., Rosete, A., Dorronsoro, B., Eds.; Springer Nature: Cham, Switzerland, 2023; pp. 63–81. [Google Scholar]
- Kesavaraj, G.; Sukumaran, S. A study on classification techniques in data mining. In Proceedings of the 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), Tiruchengode, India, 4–6 July 2013; pp. 1–7. [Google Scholar]
- Charbuty, B.; Abdulazeez, A. Classification Based on Decision Tree Algorithm for Machine Learning. J. Appl. Sci. Technol. Trends 2021, 2, 20–28. [Google Scholar] [CrossRef]
- Damanik, I.S.; Windarto, A.P.; Wanto, A.; Poningsih; Andani, S.R.; Saputra, W. Decision Tree Optimization in C4.5 Algorithm Using Genetic Algorithm. J. Phys. Conf. Ser. 2019, 1255, 012012. [Google Scholar] [CrossRef]
- Barros, R.C.; Basgalupp, M.P.; Carvalho, A.C.P.L.F.D.; Freitas, A.A. A Survey of Evolutionary Algorithms for Decision-Tree Induction. IEEE Trans. Syst. Man. Cybern. Part. C Appl. Rev. 2012, 42, 291–312. [Google Scholar] [CrossRef]
- Anuradha; Gupta, G. A self explanatory review of decision tree classifiers. In Proceedings of the International Conference on Recent Advances and Innovations in Engineering (ICRAIE-2014), Jaipur, India, 9–11 May 2014; pp. 1–7. [Google Scholar]
- Gavankar, S.S.; Sawarkar, S.D. Eager decision tree. In Proceedings of the 2017 2nd International Conference for Convergence in Technology (I2CT), Mumbai, India, 7–9 April 2017; pp. 837–840. [Google Scholar]
- Jagtap, S.T.; Phasinam, K.; Kassanuk, T.; Jha, S.S.; Ghosh, T.; Thakar, C.M. Towards application of various machine learning techniques in agriculture. Mater. Today Proc. 2022, 51, 793–797. [Google Scholar] [CrossRef]
- Zounemat-Kermani, M.; Batelaan, O.; Fadaee, M.; Hinkelmann, R. Ensemble machine learning paradigms in hydrology: A review. J. Hydrol. 2021, 598, 126266. [Google Scholar] [CrossRef]
- Persson, I.; Grünwald, A.; Morvan, L.; Becedas, D.; Arlbrandt, M. A Machine Learning Algorithm Predicting Acute Kidney Injury in Intensive Care Unit Patients (NAVOY Acute Kidney Injury): Proof-of-Concept Study. JMIR Form. Res. 2023, 7, e45979. [Google Scholar] [CrossRef] [PubMed]
- Zhou, X.; Lu, P.; Zheng, Z.; Tolliver, D.; Keramati, A. Accident Prediction Accuracy Assessment for Highway-Rail Grade Crossings Using Random Forest Algorithm Compared with Decision Tree. Reliab. Eng. Syst. Saf. 2020, 200, 106931. [Google Scholar] [CrossRef]
- Chen, R.-C.; Dewi, C.; Huang, S.-W.; Caraka, R.E. Selecting critical features for data classification based on machine learning methods. J. Big Data 2020, 7, 52. [Google Scholar] [CrossRef]
- Chen, T.; Guestrin, C. XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13–17 August 2016. [Google Scholar] [CrossRef]
- Tigga, N.P.; Garg, S. Prediction of Type 2 Diabetes using Machine Learning Classification Methods. Procedia Comput. Sci. 2020, 167, 706–716. [Google Scholar] [CrossRef]
No. | Tongue Color | Pathological Case |
---|---|---|
1 | Diabetes mellitus (DM), heat syndrome, or liver and gallbladder diseases | |
2 | Diabetes mellitus (DM2), mycotic infection, biliary ducts and gallbladder, or reduction in the body’s immune defenses | |
3 | Asthma, circulatory and digestive problems, or cancer | |
4 | Acute stroke, COVID-19, inflammation of the tongue, resistant pylori infection, or appendicitis | |
5 | Colored fungiform papillae or inflammation of the enlarged papillae | |
6 | Cold syndrome or anemia | |
7 | Normal tongue (healthy case) |
Algorithm | Parameter | Tuned Value/Range |
---|---|---|
NB | Smoothing parameter (alpha) | 0.1, 0.5, 1.0 |
SVM | Kernel type | Linear, RBF, Polynomial |
Regularization parameter (C) | 0.1, 1.0, 10.0 | |
Gamma (for RBF kernel) | 0.1, 0.01, 0.001 | |
KNN | Number of neighbors (k) | 3, 5, 7 |
Distance metric | Euclidean, Manhattan | |
DT | Maximum depth | 3, 5, 10 |
Minimum samples split | 2, 5, 10 | |
RF | Number of trees | 50, 100, 200 |
Maximum depth per tree | 5, 10, 15 | |
Minimum samples split | 2, 5, 10 | |
XGBoost | Learning rate | 0.01, 0.1, 0.3 |
Maximum depth | 3, 5, 7 | |
Number of estimators | 50, 100, 200 |
Technique | Accuracy % | Precision | Recall | F1-Score | Jaccard Index | G-Score | Zero-One Loss | Hamming Loss | Cohen’s Kappa | MCC | Fowlkes–Mallows Index |
---|---|---|---|---|---|---|---|---|---|---|---|
NB | 91.43 | 0.88 | 0.89 | 0.88 | 0.9273 | 0.6154 | 0.0857 | 0.0857 | 0.97 | 0.32 | 0.8892 |
SVM | 96.50 | 0.96 | 0.94 | 0.95 | 0.9406 | 0.8563 | 0.0350 | 0.0350 | 0.99 | 0.34 | 0.9482 |
KNN | 96.77 | 0.95 | 0.96 | 0.96 | 0.9643 | 0.8400 | 0.0323 | 0.0323 | 0.99 | 0.35 | 0.9583 |
DT | 98.06 | 0.97 | 0.97 | 0.97 | 0.9927 | 0.9000 | 0.0194 | 0.0194 | 0.99 | 0.35 | 0.9727 |
RF | 98.62 | 0.97 | 0.98 | 0.98 | 0.9826 | 0.9077 | 0.0138 | 0.0138 | 1.00 | 0.35 | 0.9780 |
XGBoost | 98.71 | 0.98 | 0.98 | 0.98 | 0.9895 | 0.9202 | 0.0129 | 0.0129 | 1.00 | 0.35 | 0.9799 |
Study Title | Year | Accuracy |
---|---|---|
Diagnosis of Diabetes from Tongue Image Using Versatile Tooth-Marked Region Classification | 2019 | 90.23% |
A tongue features fusion approach to predicting prediabetes and diabetes with machine learning | 2021 | 98.4% |
Analysis of Tongue Color-Associated Features among Patients with PCR-Confirmed COVID-19 Infection in Ukraine | 2021 | 64.29% |
Tongue Color Analysis and Diseases Detection Based on a Computer Vision System. | 2022 | 95% |
Panoramic tongue imaging and deep convolutional machine learning model for diabetes | 2022 | 98.4% |
Disease Prediction from Tongue Based on Machine Learning Algorithms | 2024 | 98.7% |
Study | Year | Features | Datasets | Algorithms/Methods |
---|---|---|---|---|
Umadevi et al. [16]. | 2019 | Implicit association method | 96 patient tongue images, 97 UV-scanned tongue images | Dental multi-labeled region approach |
Thirunavukkarasu et al. [17]. | 2019 | Thermal differences | 140 thermal tongue images | Convolutional neural networks (CNNs) |
Naveed et al. [18]. | 2020 | Tongue images | 700 tongue images | Fractional-order Darwinian particle swarm optimization |
Horzof et al. [13]. | 2021 | Tongue color, plaque color, disease severity | 135 tongue images from COVID-19 patients | Cochran–Armitage test |
Mansour et al. [19]. | 2021 | Tongue color | Not specified | IoT system, deep neural networks (DNNs) |
Chen et al. [24]. | 2021 | Tongue color | 457 tongue datasets | ID3, J48, naïve Bayes, BayesNet, SMO |
Li et al. [21]. | 2021 | Color and texture features | TFDA-1 Tongue Diagnostic Tool | Various ML algorithms |
Yang et al. [23]. | 2022 | Tooth marks, stains, fissures | Tongue image datasets | YOLOv5s6, U-Net, MobileNetV3Large |
Abdullah et al. [5]. | 2022 | Tongue color | 50 tongue images | Real-time imaging, MATLAB GUI |
Balasubramaniyan et al. [22]. | 2022 | Tooth mark, fur color, fur thickness, tongue shape, saliva, tongue color, red dot | Not specified | Deep convolutional neural network, hybrid neural network models |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
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
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 StyleHassoon, 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