Bayesian Network as a Decision Tool for Predicting ALS Disease
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 dis...
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MDPI AG
2021-01-01
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| Series: | Brain Sciences |
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| Online Access: | https://www.mdpi.com/2076-3425/11/2/150 |
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doaj-2dac633a07ee4032bee4afd6591049702021-01-24T00:03:24ZengMDPI AGBrain Sciences2076-34252021-01-011115015010.3390/brainsci11020150Bayesian Network as a Decision Tool for Predicting ALS DiseaseHasan Aykut Karaboga0Aslihan Gunel1Senay Vural Korkut2Ibrahim Demir3Resit Celik4Department of Statistics, Amasya University, Amasya 05100, TurkeyDepartment of Chemistry, Ahi Evran University, Kirsehir 40200, TurkeyDepartment of Molecular Biology and Genetics, Yildiz Technical University, Istanbul 34220, TurkeyDepartment of Statistics, Yildiz Technical University, Istanbul 34220, TurkeyDepartment of Statistics, Yildiz Technical University, Istanbul 34220, TurkeyClinical 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.https://www.mdpi.com/2076-3425/11/2/150motor neuron diseaseamyotrophic lateral sclerosisParkinson’s diseasemachine learningBayesian networkspredictive model |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Hasan Aykut Karaboga Aslihan Gunel Senay Vural Korkut Ibrahim Demir Resit Celik |
| spellingShingle |
Hasan Aykut Karaboga Aslihan Gunel Senay Vural Korkut Ibrahim Demir Resit Celik Bayesian Network as a Decision Tool for Predicting ALS Disease Brain Sciences motor neuron disease amyotrophic lateral sclerosis Parkinson’s disease machine learning Bayesian networks predictive model |
| author_facet |
Hasan Aykut Karaboga Aslihan Gunel Senay Vural Korkut Ibrahim Demir Resit Celik |
| author_sort |
Hasan Aykut Karaboga |
| title |
Bayesian Network as a Decision Tool for Predicting ALS Disease |
| title_short |
Bayesian Network as a Decision Tool for Predicting ALS Disease |
| title_full |
Bayesian Network as a Decision Tool for Predicting ALS Disease |
| title_fullStr |
Bayesian Network as a Decision Tool for Predicting ALS Disease |
| title_full_unstemmed |
Bayesian Network as a Decision Tool for Predicting ALS Disease |
| title_sort |
bayesian network as a decision tool for predicting als disease |
| publisher |
MDPI AG |
| series |
Brain Sciences |
| issn |
2076-3425 |
| publishDate |
2021-01-01 |
| description |
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. |
| topic |
motor neuron disease amyotrophic lateral sclerosis Parkinson’s disease machine learning Bayesian networks predictive model |
| url |
https://www.mdpi.com/2076-3425/11/2/150 |
| work_keys_str_mv |
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