帕金森病鉴别诊断的分类方法比较

Tutsenko K.O., Narkevich A.N., Kurbanismailov R.B., Abramov V. G.
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引用次数: 0

摘要

背景。帕金森病是一种常见的慢性神经退行性疾病,会损害生活质量。目前,还没有药物可以治愈这种疾病。早期发现病理将提高诊断和预后的准确性,并在最有效的阶段开始治疗。使用放射性药物18F-DOPA的正电子发射断层扫描可以在临床前阶段检测帕金森病患者的多巴胺能缺乏症和特发性震颤的鉴别诊断,其中多巴胺产生的神经元不受影响。本研究的目的是确定各种分类方法区分帕金森病患者与其他研究组的能力。材料和方法。该研究涉及3组:健康个体(n = 33)、帕金森病患者(n = 32)和特发性震颤患者(n = 29)。在我们的工作中使用了以下分类方法:朴素贝叶斯分类器、k近邻、随机森林、逻辑回归和人工神经网络。结果。所有考虑的方法都显示出较高的分类质量。logistic回归模型的结果最高。k近邻法的灵敏度、特异度和准确度最低。结论。数学模型将允许基于18F-DOPA PET数据的PD个体诊断,灵敏度、特异性和准确性均在95%以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
COMPARISON OF CLASSIFICATION METHODS FOR THE DIFFERENTIAL DIAGNOSIS OF PARKINSON'S DISEASE
Background. Parkinson's disease is a common chronic neurodegenerative disease that impairs the quality of life. Currently, there are no drugs that can cure this disease. Early detection of pathology will improve the accuracy of diagnosis and prognosis, as well as start therapy at the stage when it can be most effective. Positron emission tomography with the radiopharmaceutical 18F-DOPA allows the detection of dopaminergic deficiency in patients with Parkinson's disease at the preclinical stage and differential diagnosis with essential tremor, in which dopamine-producing neurons are not affected. The purpose of this study is to determine the ability of various classification methods to differentiate patients with Parkinson's disease from other study groups. Materials and methods. The study involved 3 groups: healthy individuals (n = 33), patients with Parkinson's disease (n = 32) and patients with essential tremor (n = 29). The following classification methods were used in our work: naive Bayes classifier, k-nearest neighbors, random forest, logistic regression and artificial neural network. Results. All considered methods showed high quality of classification. The logistic regression model showed the highest results. The lowest values of sensitivity, specificity and accuracy were shown by the k-nearest neighbors’ method. Conclusion. Mathematical models will allow individual diagnosis of PD based on 18F-DOPA PET data with sensitivity, specificity and accuracy above 95%.
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