Predictive Modeling Based on Machine Learning Techniques to Predicate Tooth Loss Among Adults in Nasiriyah City, Iraq

Ghosson K.  Munahy; Nabra F.  Salih; Manar Hamza  Bashah

Ghosson K. Munahy Department of Information Technology, College of Computer Science and Information Technology, University of Kerbala, Kerbala, Iraq
Nabra F. Salih College of Dentistry, University of Thi Qar, Thi Qar, Iraq
Manar Hamza Bashah Department of Information Technology, College of Computer Science and Information Technology, University of Kerbala, Kerbala, Iraq

Keywords: Machine Learning, Predictive Analytics, Tooth Loss, Random Forest

Abstract

Tooth loss negatively affects the overall physical and social well-being of adults. Controversy surrounds the socioeconomic variables that influence tooth loss, an issue that can negatively impact a person's quality of life. Better diagnostic tools and medical data analysis can be achieved in the healthcare industry through the application of machine learning. Machine learning is focused on mimicking human learning with data and algorithms and gradually increasing the model's accuracy. In order to predict adult tooth loss both completely and incrementally, this study will develop a machine-learning approach and compare the predictive performance of various models. We first collected the most data from the Dental clinics of the College of Dentistry at the University of Thi Qar. Then, we analyzed the data using statistical tools and developed a system using machine learning techniques for predicting tooth loss among adults. We found that tooth loss and socioeconomic factors are often linked, with individuals from lower socioeconomic backgrounds experiencing higher rates of tooth loss. Several factors contribute to this relationship, including access to dental care. Our predictive approach achieves high performance when we use a random forest algorithm, with results of 94.0%, 97.3, 97.1%, 97.4%, 97.3%, and 81.4% for AUC, CA, F1, Precision, Recall, and MCC, respectively.

References

M. Bari Antor, et al., "A comparative analysis of machine learning algorithms to predict Alzheimer’s disease," Journal of Healthcare Engineering, vol. 2021, no. 1, p. 9917919, 2021.

H. W. Elani, et al., "Predictors of tooth loss: A machine learning approach," PLoS One, vol. 16, no. 6, p. e0252873, 2021.

N. M. Salem, et al., "Machine and deep learning identified metabolites and clinical features associated with gallstone disease," Computer Methods and Programs in Biomedicine Update, vol. 3, p. 100106, 2023.

R. Venkatesh, C. Balasubramanian, and M. Kaliappan, "Development of big data predictive analytics model for disease prediction using machine learning technique," Journal of Medical Systems, vol. 43, no. 8, p. 272, 2019.

K. Ramudu, et al., "Machine learning and artificial intelligence in disease prediction: Applications, challenges, limitations, case studies, and future directions," in Contemporary Applications of Data Fusion for Advanced Healthcare Informatics, IGI Global, 2023, pp. 297–318.

Y. Jiang, et al., "FXAM: A unified and fast interpretable model for predictive analytics," Expert Systems with Applications, vol. 252, p. 123890, 2024.

U. Cooray, et al., "Importance of socioeconomic factors in predicting tooth loss among older adults in Japan: Evidence from a machine learning analysis," Social Science & Medicine, vol. 291, p. 114486, 2021.

C.-T. Lee, et al., "Identifying predictors of the tooth loss phenotype in a large periodontitis patient cohort using a machine learning approach," Journal of Dentistry, vol. 144, p. 104921, 2024.

S. S. Bhat, et al., "A risk assessment and prediction framework for diabetes mellitus using machine learning algorithms," Healthcare Analytics, p. 100273, 2023.

R. Ahmed, M. Bibi, and S. Syed, "Improving heart disease prediction accuracy using a hybrid machine learning approach: A comparative study of SVM and KNN algorithms," International Journal of Computations, Information and Manufacturing (IJCIM), vol. 3, no. 1, pp. 49–54, 2023.

I. Steinwart and A. Christmann, Support Vector Machines, Springer Science & Business Media, 2008.

A. Hasuike, et al., "Machine learning in predicting tooth loss: A systematic review and risk of bias assessment," Journal of Personalized Medicine, vol. 12, no. 10, p. 1682, 2022.

A. Alibrahim, et al., "Patterns and predictors of tooth loss among partially dentate individuals in Jordan: A cross-sectional study," The Saudi Dental Journal, vol. 36, no. 3, pp. 486–491, 2024.

D. Stojanov, et al., "Predicting the outcome of heart failure against chronic-ischemic heart disease in elderly population–Machine learning approach based on logistic regression, case to Villa Scassi hospital Genoa, Italy," Journal of King Saud University-Science, vol. 35, no. 3, p. 102573, 2023.

S. A. Pattekari and A. Parveen, "Prediction system for heart disease using Naïve Bayes," International Journal of Advanced Computer and Mathematical Sciences, vol. 3, no. 3, pp. 290–294, 2012.

W. Thomson, et al., "Long-term dental visiting patterns and adult oral health," Journal of Dental Research, vol. 89, no. 3, pp. 307–311, 2010.

R. Maimaitituerxun, et al., "Predictive model for identifying mild cognitive impairment in patients with type 2 diabetes mellitus: A CHAID decision tree analysis," Brain and Behavior, vol. 14, no. 3, p. e3456, 2024.

L. Breiman, "Random forests," Machine Learning, vol. 45, pp. 5–32, 2001.

J. L. Speiser, et al., "A comparison of random forest variable selection methods for classification prediction modeling," Expert Systems with Applications, vol. 134, pp. 93–101, 2019.

X. Zhang and C.-A. Liu, "Model averaging prediction by K-fold cross-validation," Journal of Econometrics, vol. 235, no. 1, pp. 280–301, 2023.