Abstract
Menopause is the permanent cessation of menstruation occurring naturally in women's aging. The most frequent symptoms associated with menopausal phases are mucosal dryness, increased weight and body fat, and changes in sleep patterns. Oral symptoms in menopause derived from saliva flow reduction can lead to dry mouth, ulcers, and alterations of taste and swallowing patterns. However, the oral health phenotype of postmenopausal women has not been characterized. The aim of the study was to determine postmenopausal women's oral phenotype, including medical history, lifestyle, and oral assessment through artificial intelligence algorithms. We enrolled 100 postmenopausal women attending the Dental School of the University of Seville were included in the study. We collected an extensive questionnaire, including lifestyle, medication, and medical history. We used an unsupervised k-means algorithm to cluster the data following standard features for data analysis. Our results showed the main oral symptoms in our postmenopausal cohort were reduced salivary flow and periodontal disease. Relying on the classical assessment of the collected data, we might have a biased evaluation of postmenopausal women. Then, we used artificial intelligence analysis to evaluate our data obtaining the main features and providing a reduced feature defining the oral health phenotype. We found 6 clusters with similar features, including medication affecting salivation or smoking as essential features to obtain different phenotypes. Thus, we could obtain main features considering differential oral health phenotypes of postmenopausal women with an integrative approach providing new tools to assess the women in the dental clinic.
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References
Marne S, Churi S, Marne M (2020) Predicting breast cancer using effective classification with decision tree and K means clustering technique, In: 2020 international conference on emerging smart computing and informatics (ESCI), Pune, India, pp 39–42. https://doi.org/10.1109/ESCI48226.2020.9167544
Rajaguru H, S R SC (2019) Analysis of decision tree and k-nearest neighbor algorithm in the classification of breast cancer. Asian Pac J Cancer Prev 20(12):3777–3781. https://doi.org/10.31557/APJCP.2019.20.12.3777. PMID: 31870121; PMCID: PMC7173366
Tu MC, Shin D, Shin D (2009) A comparative study of medical data classification methods based on decision tree and bagging algorithms. In: 2009 Eighth IEEE international conference on dependable, autonomic and secure computing, Chengdu, China, pp 183–187. https://doi.org/10.1109/DASC.2009.40
Yu Z, Fuchao C (2021) Medical and health data classification method based on machine learning. J Healthc Eng 2021:5. Article ID 2722854. https://doi.org/10.1155/2021/2722854
Sorrell S (2009) The rebound effect: definition and estimation. In: Evans J, Hunt L (eds) International handbook on the economics of energy, Cheltenham, Edward Elgar
Acknowledgements
This research has been partially supported by the “Generation of Reliable Synthetic Health Data for Federated Learning in Secure Data Spaces” Research Project (PID2022-141045OB-C42 (AEI/FEDER, UE)) funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe” by the “European Union”.
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Kraft, R., Ortega, J.A., Simón-Soro, Á., Martínez, N., González-Abril, L. (2024). Oral Health Phenotype of Postmenopausal Women Using AI. In: Bellotti, F., et al. Applications in Electronics Pervading Industry, Environment and Society. ApplePies 2023. Lecture Notes in Electrical Engineering, vol 1110. Springer, Cham. https://doi.org/10.1007/978-3-031-48121-5_31
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DOI: https://doi.org/10.1007/978-3-031-48121-5_31
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