Javeed Ashir, Dallora Ana Luiza, Berglund Johan Sanmartin, Anderberg Peter
Aging Research Center, Karolinska Institutet, 171 65 Stockholm, Sweden.
Department of Health, Blekinge Institute of Technology, 371 79 Karlskrona, Sweden.
Life (Basel). 2022 Jul 21;12(7):1097. doi: 10.3390/life12071097.
Dementia is a neurological condition that primarily affects older adults and there is still no cure or therapy available to cure it. The symptoms of dementia can appear as early as 10 years before the beginning of actual diagnosed dementia. Hence, machine learning (ML) researchers have presented several methods for early detection of dementia based on symptoms. However, these techniques suffer from two major flaws. The first issue is the bias of ML models caused by imbalanced classes in the dataset. Past research did not address this issue well and did not take preventative precautions. Different ML models were developed to illustrate this bias. To alleviate the problem of bias, we deployed a synthetic minority oversampling technique (SMOTE) to balance the training process of the proposed ML model. The second issue is the poor classification accuracy of ML models, which leads to a limited clinical significance. To improve dementia prediction accuracy, we proposed an intelligent learning system that is a hybrid of an autoencoder and adaptive boost model. The autoencoder is used to extract relevant features from the feature space and the Adaboost model is deployed for the classification of dementia by using an extracted subset of features. The hyperparameters of the Adaboost model are fine-tuned using a grid search algorithm. Experimental findings reveal that the suggested learning system outperforms eleven similar systems which were proposed in the literature. Furthermore, it was also observed that the proposed learning system improves the strength of the conventional Adaboost model by 9.8% and reduces its time complexity. Lastly, the proposed learning system achieved classification accuracy of 90.23%, sensitivity of 98.00% and specificity of 96.65%.
痴呆症是一种主要影响老年人的神经疾病，目前仍然没有治愈或治疗方法。痴呆症的症状可能早在实际确诊痴呆症的10年前就会出现。因此，机器学习（ML）研究人员提出了几种基于症状的痴呆症早期检测方法。然而，这些技术存在两个主要缺陷。第一个问题是数据集中类不平衡导致的ML模型偏差。过去的研究没有很好地解决这个问题，也没有采取预防措施。开发了不同的ML模型来说明这种偏差。为了缓解偏差问题，我们部署了合成少数过采样技术（SMOTE）来平衡所提出的ML模型的训练过程。第二个问题是ML模型的分类准确率低，这导致临床意义有限。为了提高痴呆症预测准确率，我们提出了一种智能学习系统，它是自动编码器和自适应增强模型的混合体。自动编码器用于从特征空间中提取相关特征，Adaboost模型用于使用提取的特征子集对痴呆症进行分类。使用网格搜索算法对Adaboost模型的超参数进行微调。实验结果表明，所建议的学习系统优于文献中提出的11个类似系统。此外，还观察到所提出的学习系统将传统Adaboost模型的强度提高了9.8%，并降低了其时间复杂度。最后，所提出的学习系统实现了90.23%的分类准确率、98.00%的灵敏度和96.65%的特异性。
