Idrisoglu Alper, Flyborg Johan, Nauman Ghazi Sarah, Mikaelsson Midlöv Elina, Dellkvist Helén, Axén Anna, Dallora Ana Luiza
Department of Health, Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 371 41, Sweden, 46 701462619.
JMIR Med Inform. 2025 Aug 25;13:e75069. doi: 10.2196/75069.
As the older population grows, so does the prevalence of cognitive impairment, emphasizing the importance of early diagnosis. The Mini-Mental State Examination (MMSE) is vital in identifying cognitive impairment. It is known that degraded oral health correlates with MMSE scores ≤26.
This study aims to explore the potential of using machine learning (ML) technologies using oral health and demographic examination data to predict the probability of having MMSE scores of 30 or ≤26 in Swedish individuals older than 60 years.
The study had a cross-sectional design. Baseline data from 2 longitudinal oral health and ongoing general health studies involving individuals older than 60 years were entered into ML models, including random forest, support vector machine, and CatBoost (CB) to classify MMSE scores as either 30 or ≤26, distinguishing between MMSE of 30 and MMSE ≤26 groups. Nested cross-validation (nCV) was used to mitigate overfitting. The best performance-giving model was further investigated for feature importance using Shapley additive explanation summary plots to easily visualize the contribution of each feature to the prediction output. The sample consisted of 693 individuals (350 females and 343 males).
All CB, random forest, and support vector machine models achieved high classification accuracies. However, CB exhibited superior performance with an average accuracy of 80.6% on the model using 3 × 3 nCV and surpassed the performance of other models. The Shapley additive explanation summary plot illustrates the impact of factors on the model's predictions, such as age, Plaque Index, probing pocket depth, a feeling of dry mouth, level of education, and use of dental hygiene tools for approximal cleaning.
The oral health parameters and demographic data used as inputs for ML classifiers contain sufficient information to differentiate between MMSE scores ≤26 and 30. This study suggests oral health parameters and ML techniques could offer a potential tool for screening MMSE scores for individuals aged 60 years and older.
随着老年人口的增加，认知障碍的患病率也在上升，这凸显了早期诊断的重要性。简易精神状态检查表（MMSE）在识别认知障碍方面至关重要。已知口腔健康状况下降与MMSE评分≤26相关。
本研究旨在探索利用机器学习（ML）技术，通过口腔健康和人口统计学检查数据，预测瑞典60岁以上个体MMSE评分为30或≤26的概率。
本研究采用横断面设计。将来自两项涉及60岁以上个体的纵向口腔健康和持续的一般健康研究的基线数据输入ML模型，包括随机森林、支持向量机和CatBoost（CB），以将MMSE评分分类为30或≤26，区分MMSE为30和MMSE≤26的组。采用嵌套交叉验证（nCV）来减轻过拟合。使用Shapley加法解释汇总图进一步研究性能最佳的模型的特征重要性，以便轻松可视化每个特征对预测输出的贡献。样本包括693名个体（350名女性和343名男性）。
所有CB、随机森林和支持向量机模型均取得了较高的分类准确率。然而，CB表现出卓越的性能，在使用3×3 nCV的模型上平均准确率为80.6%，超过了其他模型的性能。Shapley加法解释汇总图说明了年龄、菌斑指数、探诊袋深度、口干感觉、教育水平以及使用口腔卫生工具进行邻面清洁等因素对模型预测的影响。
用作ML分类器输入的口腔健康参数和人口统计学数据包含足够的信息来区分MMSE评分≤26和30。本研究表明，口腔健康参数和ML技术可为60岁及以上个体筛查MMSE评分提供一种潜在工具。
