Karaboga Hasan Aykut, Gunel Aslihan, Korkut Senay Vural, Demir Ibrahim, Celik Resit
Department of Statistics, Amasya University, Amasya 05100, Turkey.
Department of Statistics, Yildiz Technical University, Istanbul 34220, Turkey.
Brain Sci. 2021 Jan 23;11(2):150. doi: 10.3390/brainsci11020150.
Clinical diagnosis of amyotrophic lateral sclerosis (ALS) is difficult in the early period. But blood tests are less time consuming and low cost methods compared to other methods for the diagnosis. The ALS researchers have been used machine learning methods to predict the genetic architecture of disease. In this study we take advantages of Bayesian networks and machine learning methods to predict the ALS patients with blood plasma protein level and independent personal features. According to the comparison results, Bayesian Networks produced best results with accuracy (0.887), area under the curve (AUC) (0.970) and other comparison metrics. We confirmed that sex and age are effective variables on the ALS. In addition, we found that the probability of onset involvement in the ALS patients is very high. Also, a person's other chronic or neurological diseases are associated with the ALS disease. Finally, we confirmed that the Parkin level may also have an effect on the ALS disease. While this protein is at very low levels in Parkinson's patients, it is higher in the ALS patients than all control groups.
肌萎缩侧索硬化症（ALS）的临床诊断在早期较为困难。但与其他诊断方法相比，血液检测是耗时较少且成本较低的方法。ALS研究人员一直使用机器学习方法来预测疾病的遗传结构。在本研究中，我们利用贝叶斯网络和机器学习方法，根据血浆蛋白水平和个人独立特征来预测ALS患者。根据比较结果，贝叶斯网络在准确率（0.887）、曲线下面积（AUC）（0.970）和其他比较指标方面产生了最佳结果。我们证实性别和年龄是影响ALS的有效变量。此外，我们发现ALS患者发病累及的概率非常高。而且，一个人的其他慢性或神经疾病与ALS疾病相关。最后，我们证实帕金森蛋白水平可能也对ALS疾病有影响。虽然这种蛋白在帕金森病患者中水平极低，但在ALS患者中比所有对照组都更高。
