Predictive Analysis of Periodontal Disease Progression Using Machine Learning: Enhancing Oral Health Assessment and Treatment Planning
Keywords:
Periodontitis, dental disease prediction, machine learning, feature selection, feature engineering, accuracy, oral health, decision making, COVID 19, supervised learning, classifiers, digitalization, artificial intelligence, data analysis, gum disease, cross validationAbstract
Artificial Intelligence (AI) has revolutionized various aspects of our lives, offering solutions to numerous problems and bridging gaps between reality and business. Within the realm of AI, emerging technologies such as machine learning and deep learning have become prominent in transforming the way we analyze data, make decisions, and address challenges. With the exponential growth of data usage and storage, these technologies have assumed a vital role in data analytics, storage management, and decision-making processes. As digital transformation continues to reshape industries and services worldwide, the healthcare sector, including oral health services, necessitates complete digitalization. Oral diseases, prevalent across all age groups, often go neglected until they reach a painful and severe stage, leading to potential tooth damage. To counteract such consequences, the field of dentistry requires digitalization for timely diagnosis, effective decision-making, patient management, and predictive capabilities. This research paper focuses on leveraging these emerging technologies to predict the progression of Periodontitis, a common oral disease. In this study, machine learning classifiers are employed to analyze and predict the disease. Additionally, cross-validation methods, feature extraction techniques, and ensemble learning strategies are implemented and evaluated. The performance metrics are compared for various classifiers including Naïve Bayes, Support Vector Machine, Random Forest, Logistic Regression, K Nearest Neighbors, and Decision Tree classifiers. These classifiers are applied to a dataset of 1000 periodontitis patients, resulting in impressive accuracies of 95.5%, 100%, 100%, 100%, 99.5%, and 99% respectively, for the classification of chronic localized and chronic generalized periodontitis. Through this research, we highlight the potential of machine learning and other AI techniques in revolutionizing the field of dentistry. By harnessing the power of predictive analysis, accurate diagnosis, timely interventions, and improved patient management can be achieved, ultimately enhancing oral health outcomes. This study serves as a significant step towards integrating advanced technologies into dentistry, contributing to the overall digital transformation of the healthcare industry.
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References
RM Baiju, Elbe Peter etal, “Oral Health and Quality of Life: Current Concepts”, Journal of Clinical and Diagnostic Research, Jun 2017, 11(6): ZE21-ZE26
Xiaojing Li, Kristin M. Kolltveit etal, “Systemic Diseases Caused by Oral diseases”, Clinical Microbiology Reviews, Oct 2000, 13(4): 547-558
Eija Könönen, Mervi Gursoy etal, “Periodontitis: A Multifaceted Disease of Tooth-Supporting Tissues”, Journal of Clinical Medicine”, Jul 2019, 8(8):1135
TK Madiba and A Bhayat, “Periodontal disease - risk factors and treatment options”, South African dental journal, Oct 2018, 73(9): 571 - 575
Arkadiusz Dziedzic and Robert Wojtyczka, “The impact of coronavirus infectious disease 19 (COVID-19) on oral health”, Oral diseases, Apr 2020,00:1-4
Muhammad Ashraf Nazir, “Prevalence of periodontal disease, its association with systemic diseases and prevention”, International Journal of Health Sciences, Jun 2017, 1(2):72-80
Divya Tandona and Jyotika Rajawata, “Present and future of artificial intelligence in dentistry”, Journal of Oral Biology and Craniofacial Research, Dec 2020, 10(4):391-396
Neel Shimpi , Susan McRoy etal, “Development of a periodontitis risk assessment model for primary care providers in an interdisciplinary setting”, Technology and Health Care, May 2019,1: 1–12
Asghar Tabatabaei Balaei, Philip de Chazal etal, “Automatic Detection of Periodontitis Using Intra-Oral Images”, IEEE 2017,3906-3909
Jaehan Joo, Sinjin Jeong etal, “Periodontal Disease Detection Using Convolutional Neural Networks”, IEEE ICAIIC 2019, 360-362
Maryam Farhadian, Parisa Shokouhi etal, “A decision support system based on support vector machine for diagnosis of periodontal disease”, BMC Res Notes, Jul 2020, 13:337
Gopi Battineni, Getu Gamo Sagaro etal, “Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis”, Journal of personalized medicine, Jun 2020, 10(2):21
Eun-Hye Kim, Seunghoon Kim etal, “Prediction of Chronic Periodontitis Severity Using Machine Learning Models Based On Salivary Bacterial Copy Number”, Frontiers in Cellular and Infection Microbiology, Nov 2020, 10:698
Sanjeev B. Khanagar, Ali Al-ehaideb etal, “Developments, application, and performance of artificial intelligence in dentistry A systematic review”, Journal of dental sciences, Jan 2021, 16(1):508-522
S. B. Kotsiantis, “Feature selection for machine learning classification problems: a recent overview”, Artificial Intelligence Review, May 2011
Isabelle Guyon and Andre Elisseeff, “An Introduction to Variable and Feature Selection”, Journal of Machine Learning Research, Mar 2003, 3:1157-1182
Jie Cai, Jiawei Luo etal, “Feature selection in machine learning: A new perspective”, Neurocomputing, Jul 2018, 300: 70-79
Chuan Liua, Wenyong Wang etal, “A new feature selection method based on a validity index of feature subset”, Pattern recognition letters, Jun 2017, 92:1-8
A. Jovic, K. Brkic etal, “A review of feature selection methods with applications”, IEEE 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Jul 2015, 1200-1205
Luka Cehovin and Zoran Bosnic, “Empirical evaluation of feature selection methods in classification”, Intelligent data analysis, Jan 2010, 14(3):265-281
Maurizio S. Tonetti, Henry Greenwell etal, “Staging and grading of periodontitis: Framework and proposal of a new classification and case definition”, Journal of clinical periodontology, Feb 2018, 45(20): 149-161
Centers for Disease control and prevention website
https://www.cdc.gov/oralhealth/conditions/periodontal-disease.html/Causes
Meliha Germen, Ulku Baser etal, “Periodontitis Prevalence, Severity, and Risk Factors: A Comparison of the AAP/CDC Case Definition and the EFP/AAP Classification”, International Journal of Environmental Research and Public Health, Mar 2021, 18:3459-3466
Denis F. Kinane, Melanie Peterson etal, “Environmental and other modifying factors of the periodontal diseases”, Periodontology 2000, Feb 2006, 40:107-119
Stanley P. Hazen, “Indices for the measurement of gingival inflammation in clinical studies of oral hygiene and periodontal disease”, Journal of Periodontal research, 1974, 9(14): 61-77
Anitha Subbappa, Aruna Ganganna etal, “Russell’s Periodontal Index: To Score or not to score”, International Journal of Information Research and Review, December, 2019, 6(12): 6647-6649
Dr. Amit Mani, Dr. Rosiline James etal, “Classifications for Furcation Involvement”, Galore International Journal of Health Sciences and Research, Mar 2018, 3(1):15-17
Larry Laster, Kenneth W. Laudenbach, “An Evaluation of Clinical Tooth Mobility Measurement”, Journal of Periodontal, Oct 1975, 46(10):603-607
Nigel G. Clarke, Robert S. Hirsch etal, “Periodontitis and angular alveolar lesions: A critical distinction”, Oral surgery oral medicine oral pathology, May 1990, 69(5):564-571
W. Peter Nordland, and Dennis P. Tarnow, “A Classification System for Loss of Papillary Height”, Journal of Periodontol, Oct 1998, 69(10):1124-1126
A. Sivakumar and R.Gunasundari, “A Survey on Data Pre-processing Techniques for Bioinformatics and Web Usage Mining”, International Journal of Pure and Applied Mathematics, 2017, 117(20):785-794
Cheng Fan, Meiling Chen etal, “A Review on Data Pre-processing Techniques Toward Efficient and Reliable Knowledge Discovery From Building Operational Data”, Frontiers in energy research, Mar 2021, 9
Salkind, Neil J, “Winsorize”, Encyclopaedia of Research Design: SAGE Publications, 2010
Xue Ying, “An Overview of Over fitting and its Solutions”, IOP Conf. Series: Journal of Physics 1168 022022, 2019
Daniel Berrar, “Cross-validation”, Encyclopaedia of Bioinformatics and Computational Biology, Apr 2019, 1: 542-545
Esra Mahsereci Karabuluta , Selma Ayşe Özel etal, “A comparative study on the effect of feature selection on classification accuracy”, Procedia Technology, May 2012, 1:323-327
David Opitz and Richard Maclin, “Popular Ensemble Methods: An Empirical Study”, Journal of Artificial Intelligence Research, Aug 1999, 11: 169-198
Kristina Machova, Miroslav Puszta etal, “A comparison of the bagging and the boosting methods using the decision tree classifiers”, Computer science and information systems, Dec 2006, 3(2):57-72
Yangguang Liu, Yangming Zhou etal, “A Strategy on Selecting Performance Metrics for Classifier Evaluation”, International Journal of Mobile Computing and Multimedia Communications, Dec 2014, 6(4): 20-35
Lakshmi TK, Dheeba J, “Digitalization in Dental problem diagnosis, Prediction and Analysis: A Machine Learning Perspective of Periodontitis”, International Journal of Recent Technology and Engineering, Jan 2020, 8(5): 67-74
Michael W Browne, “Cross-Validation Methods”, Journal of Mathematical Psychology, Mar 2000, 44(1):108-132
Lakshmi, T. K., and J. Dheeba. "Digitalization in dental problem diagnosis, prediction and analysis: A machine learning perspective of periodontitis." Int. J. Recent Technol. Eng 8.5 (2020): 67-74.
T.K., Lakshmi; DHEEBA, J.. Classification and Segmentation of Periodontal Cyst for Digital Dental Diagnosis Using Deep Learning. Computer Assisted Methods in Engineering and Science, [S.l.], v. 30, n. 2, p. 131–149, oct. 2022.
Lakshmi, T. K., and J. Dheeba. "Digital Decision Making in Dentistry: Analysis and Prediction of Periodontitis Using Machine Learning Approach." International Journal of Next-Generation Computing 13.3 (2022).
Mr. B. Naga Rajesh. (2019). Effective Morphological Transformation and Sub-pixel Classification of Clustered Images. International Journal of New Practices in Management and Engineering, 8(01), 08 - 14. https://doi.org/10.17762/ijnpme.v8i01.74
Tanaka, A., Min-ji, K., Silva, C., Cohen, D., & Mwangi, J. Predictive Analytics for Healthcare Resource Allocation. Kuwait Journal of Machine Learning, 1(4). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/150
Keerthi, R. S., Dhabliya, D., Elangovan, P., Borodin, K., Parmar, J., & Patel, S. K. (2021). Tunable high-gain and multiband microstrip antenna based on liquid/copper split-ring resonator superstrates for C/X band communication. Physica B: Condensed Matter, 618 doi:10.1016/j.physb.2021.413203
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