Bayesian Network as a Decision Tool for Predicting ALS Disease
Yazarlar (5)
Amasya Üniversitesi, Türkiye
Prof. Dr. Aslıhan GÜNEL
Kırşehir Ahi Evran Üniversitesi, Türkiye
Yıldız Teknik Üniversitesi, Türkiye
Yıldız Teknik Üniversitesi, Türkiye
Yıldız Teknik Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Brain Sciences (Q3) | ||
| Dergi ISSN | 2076-3425 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 01-2021 |
| Cilt / Sayı / Sayfa | 11 / 2 / 1–16 | DOI | 10.3390/brainsci11020150 |
| Makale Linki | https://www.mdpi.com/2076-3425/11/2/150/pdf | ||
| Özet |
| 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. |
| Anahtar Kelimeler |
| Amyotrophic lateral sclerosis | Bayesian networks | Machine learning | Motor neuron disease | Parkinson’s disease | Predictive model |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| SCOPUS | 17 |
| Google Scholar | 22 |