Clinical decision support system for early detection of Alzheimer's disease using an enhanced gradient boosted decision tree classifier
Clinical decision support system for early detection of Alzheimer's disease using an enhanced gradient boosted decision tree classifier
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DOI：
10.1177/14604582221082868
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发表时间：
2022-01-01
影响因子：
3
通讯作者：
Wilson, Robert S.
中科院分区：
文献类型：
--
作者：
Jayashree, P.;Sudha, Janaka G.;Wilson, Robert S.
阿尔茨海默病（AD）是最常见的痴呆症之一，占70%以上的病例。除生活方式和环境因素外，阿尔茨海默病等神经退行性疾病的病因和进展主要是遗传因素。早期和准确的诊断，使从业人员能够采取及时的临床决策和预防措施。这是动机，工作提出了一种新的模式匹配和评分方法对遗传物质设计一个有效的分类器。我们提出了一种独特的致病基因序列模式识别，使用后缀树作为基础检测模型，线性时间复杂度精度为91.5%。实施了一种评分机制，根据与基因相关的致病和抗病单核苷酸多态性的严重程度为基因分配分数。然后，这些分数被用作梯度增强决策树分类器的显著特征，以增强AD与健康对照的分类。在ADNI基准数据集上对所提出的基因驱动EGBDT分类器进行了效率评估，预测准确率为94.16%，与最近的文献工作相比，效率更高。
Alzheimer's disease (AD) is one of the most common forms of dementia contributing to more than 70% of the cases. The factors accounting for the cause and progression of neurodegenerative diseases like AD are primarily genetic, in addition to life style and environmental factors. Early and accurate diagnoses of AD empower practitioners to take timely clinical decisions and preventive actions. This being the motivation, the work proposes a novel pattern matching and scoring method on genetic material towards devising an effective classifier. We propose a distinctive disease causing gene sequence pattern identification using suffix trees as a base detection model with an accuracy of 91.5% in linear time complexity. A scoring mechanism is implemented to assign scores to genes based on the severity of the disease causing and disease resistant Single Nucleotide Polymorphisms associated with the genes. These scores are then used as a remarkable feature in the gradient boosted decision tree classifier to enhance the classification of AD versus healthy control. The efficiency of the proposed gene powered EGBDT classifier is evaluated on ADNI benchmark data set with the prediction accuracy of 94.16% and is found to be efficient compared to the recent works in the literature.