# Bayesian Network as a Decision Tool for Predicting ALS Disease

^{1}

^{2}

^{3}

^{4}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Participants

#### 2.2. Bayesian Networks

#### 2.3. Other Machine Learning Methods

#### 2.4. Classification Criteria

## 3. Results

#### 3.1. Bayesian Network Model

#### 3.2. Comparison Results of Methods

#### 3.3. Queries of Bayesian Network Model

## 4. Discussion and Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Acknowledgments

## Conflicts of Interest

## References

- Rowland, L.P.; Shneider, N.A. Amyotrophic Lateral Sclerosis. N. Engl. J. Med.
**2001**, 344, 1688–1700. [Google Scholar] [CrossRef] [PubMed] - Hardiman, O.; van den Berg, L.H.; Kiernan, M.C. Clinical Diagnosis and Management of Amyotrophic Lateral Sclerosis. Nat. Rev. Neurol.
**2011**, 7, 639–649. [Google Scholar] [CrossRef] [PubMed] - Swinnen, B.; Robberecht, W. The Phenotypic Variability of Amyotrophic Lateral Sclerosis. Nat. Rev. Neurol.
**2014**, 10, 661. [Google Scholar] [CrossRef] [PubMed][Green Version] - Al-Chalabi, A.; Hardiman, O. The Epidemiology of ALS: A Conspiracy of Genes, Environment and Time. Nat. Rev. Neurol.
**2013**, 9, 617. [Google Scholar] [CrossRef] [PubMed] - Filippini, T.; Fiore, M.; Tesauro, M.; Malagoli, C.; Consonni, M.; Violi, F.; Arcolin, E.; Iacuzio, L.; Oliveri Conti, G.; Cristaldi, A.; et al. Clinical and Lifestyle Factors and Risk of Amyotrophic Lateral Sclerosis: A Population-Based Case-Control Study. Int. J. Environ. Res. Public Health
**2020**, 17, 857. [Google Scholar] [CrossRef] [PubMed][Green Version] - Mendonça, D.M.F.; Pizzati, L.; Mostacada, K.; Martins, S.C.D.S.; Higashi, R.; Sá, L.A.; Neto, V.M.; Chimelli, L.; Martinez, A.M.B. Neuroproteomics: An insight into ALS. Neurol. Res.
**2012**, 34, 937–943. [Google Scholar] [CrossRef] - Manjaly, Z.R.; Scott, K.M.; Abhinav, K.; Wijesekera, L.; Ganesalingam, J.; Goldstein, L.H.; Janssen, A.; Dougherty, A.; Willey, E.; Stanton, B.R.; et al. The Sex Ratio in Amyotrophic Lateral Sclerosis: A Population Based Study. Amyotroph. Lateral Scler.
**2010**, 11, 439–442. [Google Scholar] [CrossRef] - Pape, J.A.; Grose, J.H. The Effects of Diet and Sex in Amyotrophic Lateral Sclerosis. Revue Neurol.
**2020**, 176, 301–315. [Google Scholar] [CrossRef] - Longinetti, E.; Fang, F. Epidemiology of Amyotrophic Lateral Sclerosis: An Update of Recent Literature. Curr. Opin. Neurol.
**2019**, 32, 771. [Google Scholar] [CrossRef] - Le Gall, L.; Anakor, E.; Connolly, O.; Vijayakumar, U.G.; Duddy, W.J.; Duguez, S. Molecular and Cellular Mechanisms Affected in ALS. J. Pers. Med.
**2020**, 10, 101. [Google Scholar] [CrossRef] - Brooks, B.R.; Miller, R.G.; Swash, M.; Munsat, T.L. El Escorial Revisited: Revised Criteria for the Diagnosis of Amyotrophic Lateral Sclerosis. Amyotroph. Lateral Scler. Other Mot. Neuron Disord.
**2000**, 1, 293–299. [Google Scholar] [CrossRef] [PubMed] - Vasilopoulou, C.; Morris, A.P.; Giannakopoulos, G.; Duguez, S.; Duddy, W. What Can Machine Learning Approaches in Genomics Tell Us about the Molecular Basis of Amyotrophic Lateral Sclerosis? J. Pers. Med.
**2020**, 10, 247. [Google Scholar] [CrossRef] [PubMed] - Pearl, J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference; Revised Second Printing; Morgan Kaufmann: San Francisco, CA, USA, 2014; ISBN 978-0-08-051489-5. [Google Scholar]
- Bandyopadhyay, S.; Wolfson, J.; Vock, D.M.; Vazquez-Benitez, G.; Adomavicius, G.; Elidrisi, M.; Johnson, P.E.; OConnor, P.J. Data Mining for Censored Time-to-Event Data: A Bayesian Network Model for Predicting Cardiovascular Risk from Electronic Health Record Data. Data Min. Knowl. Discov.
**2015**, 29, 1033–1069. [Google Scholar] [CrossRef][Green Version] - Kanwar, M.K.; Lohmueller, L.C.; Kormos, R.L.; Teuteberg, J.J.; Rogers, J.G.; Lindenfeld, J.; Bailey, S.H.; McIlvennan, C.K.; Benza, R.; Murali, S.; et al. A Bayesian Model to Predict Survival after Left Ventricular Assist Device Implantation. JACC Heart Fail.
**2018**, 6, 771–779. [Google Scholar] [CrossRef] [PubMed] - Kraisangka, J.; Druzdzel, M.J.; Benza, R.L. A Risk Calculator for the Pulmonary Arterial Hypertension Based on a Bayesian Network. In Proceedings of the [email protected] UAI, New York, NY, USA, 29 June 2016; pp. 1–6. [Google Scholar]
- Arora, P.; Boyne, D.; Slater, J.J.; Gupta, A.; Brenner, D.R.; Druzdzel, M.J. Bayesian Networks for Risk Prediction Using Real-World Data: A Tool for Precision Medicine. Value Health
**2019**, 22, 439–445. [Google Scholar] [CrossRef] [PubMed][Green Version] - Gupta, A.; Slater, J.J.; Boyne, D.; Mitsakakis, N.; Béliveau, A.; Druzdzel, M.J.; Brenner, D.R.; Hussain, S.; Arora, P. Probabilistic Graphical Modeling for Estimating Risk of Coronary Artery Disease: Applications of a Flexible Machine-Learning Method. Med. Decis. Mak.
**2019**, 39, 1032–1044. [Google Scholar] [CrossRef] [PubMed] - Lam, W.; Bacchus, F. Learning Bayesian Belief Networks: An Approach Based on the Mdl Principle. Comput. Intell.
**1994**, 10, 269–293. [Google Scholar] [CrossRef][Green Version] - Koller, D.; Friedman, N. Probabilistic Graphical Models. Principles and Techniques; MIT Press: Cambridge, MA, USA, 2009. [Google Scholar]
- Probabilistic Modeling in Bioinformatics and Medical Informatics; Husmeier, D.; Dybowski, R.; Roberts, S. (Eds.) Advanced Information and Knowledge Processing; Springer: London, UK, 2005; ISBN 978-1-85233-778-0. [Google Scholar]
- Senders, J.T.; Staples, P.C.; Karhade, A.V.; Zaki, M.M.; Gormley, W.B.; Broekman, M.L.D.; Smith, T.R.; Arnaout, O. Machine Learning and Neurosurgical Outcome Prediction: A Systematic Review. World Neurosurg.
**2018**, 109, 476–486. [Google Scholar] [CrossRef] - Deo Rahul, C. Machine Learning in Medicine. Circulation
**2015**, 132, 1920–1930. [Google Scholar] [CrossRef][Green Version] - Yu, K.-H.; Beam, A.L.; Kohane, I.S. Artificial Intelligence in Healthcare. Nat. Biomed. Eng.
**2018**, 2, 719–731. [Google Scholar] [CrossRef] - Dreiseitl, S.; Ohno-Machado, L. Logistic Regression and Artificial Neural Network Classification Models: A Methodology Review. J. Biomed. Inform.
**2002**, 35, 352–359. [Google Scholar] [CrossRef][Green Version] - Gevrey, M.; Dimopoulos, I.; Lek, S. Review and Comparison of Methods to Study the Contribution of Variables in Artificial Neural Network Models. Ecol. Model.
**2003**, 160, 249–264. [Google Scholar] [CrossRef] - Christodoulou, E.; Ma, J.; Collins, G.S.; Steyerberg, E.W.; Verbakel, J.Y.; Van Calster, B. A Systematic Review Shows No Performance Benefit of Machine Learning over Logistic Regression for Clinical Prediction Models. J. Clin. Epidemiol.
**2019**, 110, 12–22. [Google Scholar] [CrossRef] - Hosmer, D.W., Jr.; Lemeshow, S.; Sturdivant, R.X. Applied Logistic Regression; John Wiley & Sons: Hoboken, NJ, USA, 2013; Volume 398. [Google Scholar]
- Kleinbaum, D.G.; Dietz, K.; Gail, M.; Klein, M.; Klein, M. Logistic Regression; Springer: New York, NY, USA, 2002. [Google Scholar]
- John, G.; Langley, P. Estimating Continuous Distributions in Bayesian Classifiers. In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence; Morgan Kaufmann: San Mateo, CA, USA, 1995; pp. 338–345. [Google Scholar]
- Wei, W.; Visweswaran, S.; Cooper, G.F. The Application of Naive Bayes Model Averaging to Predict Alzheimer’s Disease from Genome-Wide Data. J. Am. Med. Inform. Assoc.
**2011**, 18, 370–375. [Google Scholar] [CrossRef] [PubMed][Green Version] - Jiang, W.; Shen, Y.; Ding, Y.; Ye, C.; Zheng, Y.; Zhao, P.; Liu, L.; Tong, Z.; Zhou, L.; Sun, S.; et al. A Naive Bayes Algorithm for Tissue Origin Diagnosis (TOD-Bayes) of Synchronous Multifocal Tumors in the Hepatobiliary and Pancreatic System. Int. J. Cancer
**2018**, 142, 357–368. [Google Scholar] [CrossRef][Green Version] - Rokach, L.; Maimon, O. Decision Trees. In Data Mining and Knowledge Discovery Handbook; Maimon, O., Rokach, L., Eds.; Springer US: Boston, MA, USA, 2005; pp. 165–192. ISBN 978-0-387-25465-4. [Google Scholar]
- Kaur, G.; Chhabra, A. Improved J48 Classification Algorithm for the Prediction of Diabetes. Int. J. Comput. Appl.
**2014**, 98, 13–17. [Google Scholar] [CrossRef] - Quinlan, J.R. Improved Use of Continuous Attributes in C4. 5. J. Artif. Intell. Res.
**1996**, 4, 77–90. [Google Scholar] [CrossRef][Green Version] - Yadav, A.K.; Chandel, S. Solar Energy Potential Assessment of Western Himalayan Indian State of Himachal Pradesh Using J48 Algorithm of WEKA in ANN Based Prediction Model. Renew. Energy
**2015**, 75, 675–693. [Google Scholar] [CrossRef] - bin Othman, M.F.; Yau, T.M.S. Comparison of Different Classification Techniques Using WEKA for Breast Cancer. In Proceedings of the 3rd Kuala Lumpur International Conference on Biomedical Engineering 2006, Kuala Lumpur, Malaysia, 11–14 December 2006; Ibrahim, F., Osman, N.A.A., Usman, J., Kadri, N.A., Eds.; Springer: Berlin/Heidelberg, Germany, 2007; pp. 520–523. [Google Scholar]
- Alpaydin, E. Introduction to Machine Learning; MIT Press: Cambridge, MA, USA, 2004. [Google Scholar]
- Schölkopf, B.; Smola, A.J. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond; Adaptive Computation and Machine Learning; MIT Press: Cambridge, MA, USA, 2009; ISBN 0-262-19475-9. [Google Scholar]
- Cristianini, N.; Shawe-Taylor, J. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods; Cambridge University Press: Cambridge, UK, 2000; ISBN 0-521-78019-5. [Google Scholar]
- Witten, I.H.; Frank, E.; Hall, M.A.; Pal, C.J. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, 4th ed.; Morgan Kaufmann Publishers: Burlington, MA, USA, 2017; ISBN 1-55860-552-5. [Google Scholar]
- Pinto, D.; Tovar, M.; Vilarino, D.; Beltrán, B.; Jiménez-Salazar, H.; Campos, B. BUAP: Performance of K-Star at the INEX’09 Clustering Task. In Proceedings of the International Workshop of the Initiative for the Evaluation of XML Retrieval, Brisbane, QLD, Australia, 7–9 December 2009; pp. 434–440. [Google Scholar]
- Painuli, S.; Elangovan, M.; Sugumaran, V. Tool Condition Monitoring Using K-Star Algorithm. Expert Syst. Appl.
**2014**, 41, 2638–2643. [Google Scholar] [CrossRef] - Wiharto, W.; Kusnanto, H.; Herianto, H. Intelligence System for Diagnosis Level of Coronary Heart Disease with K-Star Algorithm. Healthc. Inform. Res.
**2016**, 22, 30–38. [Google Scholar] [CrossRef][Green Version] - Zhang, S.; Cheng, D.; Deng, Z.; Zong, M.; Deng, X. A Novel KNN Algorithm with Data-Driven k Parameter Computation. Pattern Recognit. Lett.
**2018**, 109, 44–54. [Google Scholar] [CrossRef] - Filiz, E.; Öz, E. Educational Data Mining Methods For Timss 2015 Mathematics Success: Turkey Case. Sigma J. Eng. Nat. Sci. /Mühendislik ve Fen Bilimleri Dergisi
**2020**, 38, 963–977. [Google Scholar] - Ballabio, D.; Grisoni, F.; Todeschini, R. Multivariate Comparison of Classification Performance Measures. Chemom. Intell. Lab. Syst.
**2018**, 174, 33–44. [Google Scholar] [CrossRef] - Tharwat, A. Classification Assessment Methods. Appl. Comput. Inform.
**2020**. [Google Scholar] [CrossRef] - Kuncheva, L.I.; Arnaiz-González, Á.; Díez-Pastor, J.-F.; Gunn, I.A.D. Instance Selection Improves Geometric Mean Accuracy: A Study on Imbalanced Data Classification. Prog. Artif. Intell.
**2019**, 8, 215–228. [Google Scholar] [CrossRef][Green Version] - Marsland, S. Machine Learning: An Algorithmic Perspective, 2nd ed.; CRC Press: Boca Raton, FL, USA, 2015; ISBN 978-1-4665-8333-7. [Google Scholar]
- Sakr, S.; Elshawi, R.; Ahmed, A.M.; Qureshi, W.T.; Brawner, C.A.; Keteyian, S.J.; Blaha, M.J.; Al-Mallah, M.H. Comparison of Machine Learning Techniques to Predict All-Cause Mortality Using Fitness Data: The Henry Ford ExercIse Testing (FIT) Project. BMC Med. Inform. Decis. Mak.
**2017**, 17, 174. [Google Scholar] [CrossRef][Green Version] - Akosa, J. Predictive Accuracy: A Misleading Performance Measure for Highly Imbalanced Data. In Proceedings of the SAS Global Forum, Oklahoma State University, Orlando, FL, USA, 2–5 April 2017; pp. 2–5. [Google Scholar]
- Fawcett, T. An Introduction to ROC Analysis. Pattern Recognit. Lett.
**2006**, 27, 861–874. [Google Scholar] [CrossRef] - BayesFusion, L. GeNIe Modeler User Manual; BayesFusion, LLC: Pittsburgh, PA, USA, 2017. [Google Scholar]
- Powers, D. Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation. J. Mach. Learn. Technol.
**2011**, 2, 37–63. [Google Scholar] - Viera, A.J.; Garrett, J.M. Understanding Interobserver Agreement: The Kappa Statistic. Fam. Med.
**2005**, 37, 360–363. [Google Scholar] - Spiegelhalter, D.J.; Dawid, A.P.; Lauritzen, S.L.; Cowell, R.G. Bayesian Analysis in Expert Systems. Stat. Sci.
**1993**, 8, 219–247. [Google Scholar] [CrossRef] - Jensen, F.V.; Nielsen, T.D. Bayesian Networks and Decision Graphs, 2nd ed.; Information Science and Statistics; Springer: New York, NY, USA, 2007; ISBN 978-0-387-68281-5. [Google Scholar]
- Lin, J.-H.; Haug, P.J. Exploiting Missing Clinical Data in Bayesian Network Modeling for Predicting Medical Problems. J. Biomed. Inform.
**2008**, 41, 1–14. [Google Scholar] [CrossRef] [PubMed] - Korb, K.B.; Nicholson, A.E. Bayesian Artificial Intelligence; CRC Press: Boca Raton, FL, USA, 2011; p. 479. [Google Scholar]
- Chen, R.; Herskovits, E.H. Clinical Diagnosis Based on Bayesian Classification of Functional Magnetic-Resonance Data. Neuroinformatics
**2007**, 5, 178–188. [Google Scholar] [CrossRef] [PubMed] - Luo, Y.; El Naqa, I.; McShan, D.L.; Ray, D.; Lohse, I.; Matuszak, M.M.; Owen, D.; Jolly, S.; Lawrence, T.S.; Kong, F.-M.; et al. Unraveling Biophysical Interactions of Radiation Pneumonitis in Non-Small-Cell Lung Cancer via Bayesian Network Analysis. Radiother. Oncol.
**2017**, 123, 85–92. [Google Scholar] [CrossRef] [PubMed][Green Version] - Nojavan, A.F.; Qian, S.S.; Stow, C.A. Comparative Analysis of Discretization Methods in Bayesian Networks. Environ. Model. Softw.
**2017**, 87, 64–71. [Google Scholar] [CrossRef] - Yang, Y.; Webb, G.I. A Comparative Study of Discretization Methods for Naive-Bayes Classifiers. In Proceedings of the PKAW 2002: The 2002 Pacific Rim Knowledge Acquisition Workshop, Tokyo, Japan, 18–19 August 2002; pp. 159–173. [Google Scholar]
- Rodríguez-López, V.; Cruz-Barbosa, R. Improving Bayesian Networks Breast Mass Diagnosis by Using Clinical Data. In Proceedings of the Pattern Recognition, Mexico City, Mexico, 24–27 June 2015; Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Sossa-Azuela, J.H., Olvera López, J.A., Famili, F., Eds.; Springer International Publishing: Cham, Switzerland, 2015; pp. 292–301. [Google Scholar]
- Nagarajan, R.; Scutari, M.; Lèbre, S. Bayesian Networks in R: With Applications in Systems Biology; Use R! Springer: New York, NY, USA, 2013; ISBN 978-1-4614-6445-7. [Google Scholar]
- Antal, P.; Fannes, G.; Timmerman, D.; Moreau, Y.; De Moor, B. Using Literature and Data to Learn Bayesian Networks as Clinical Models of Ovarian Tumors. Artif. Intell. Med.
**2004**, 30, 257–281. [Google Scholar] [CrossRef] - Khanna, S.; Domingo-Fernández, D.; Iyappan, A.; Emon, M.A.; Hofmann-Apitius, M.; Fröhlich, H. Using Multi-Scale Genetic, Neuroimaging and Clinical Data for Predicting Alzheimer’s Disease and Reconstruction of Relevant Biological Mechanisms. Sci. Rep.
**2018**, 8, 11173. [Google Scholar] [CrossRef][Green Version] - Palmieri, A.; Mento, G.; Calvo, V.; Querin, G.; D’Ascenzo, C.; Volpato, C.; Kleinbub, J.R.; Bisiacchi, P.S.; Sorarù, G. Female Gender Doubles Executive Dysfunction Risk in ALS: A Case-Control Study in 165 Patients. J. Neurol. Neurosurg. Psychiatry
**2015**, 86, 574–579. [Google Scholar] [CrossRef] - Trojsi, F.; D’Alvano, G.; Bonavita, S.; Tedeschi, G. Genetics and Sex in the Pathogenesis of Amyotrophic Lateral Sclerosis (ALS): Is There a Link? Int. J. Mol. Sci.
**2020**, 21, 3647. [Google Scholar] [CrossRef] - Chiò, A.; Moglia, C.; Canosa, A.; Manera, U.; D’Ovidio, F.; Vasta, R.; Grassano, M.; Brunetti, M.; Barberis, M.; Corrado, L.; et al. ALS Phenotype Is Influenced by Age, Sex, and Genetics: A Population-Based Study. Neurology
**2020**, 94, e802–e810. [Google Scholar] [CrossRef] - Ingre, C.; Roos, P.M.; Piehl, F.; Kamel, F.; Fang, F. Risk Factors for Amyotrophic Lateral Sclerosis. Clin. Epidemiol.
**2015**, 7, 181–193. [Google Scholar] [CrossRef][Green Version] - Trojsi, F.; Siciliano, M.; Femiano, C.; Santangelo, G.; Lunetta, C.; Calvo, A.; Moglia, C.; Marinou, K.; Ticozzi, N.; Ferro, C.; et al. Comparative Analysis of C9orf72 and Sporadic Disease in a Large Multicenter ALS Population: The Effect of Male Sex on Survival of C9orf72 Positive Patients. Front. Neurosci.
**2019**, 13, 485. [Google Scholar] [CrossRef] [PubMed] - Rooney, J.; Fogh, I.; Westeneng, H.-J.; Vajda, A.; McLaughlin, R.; Heverin, M.; Jones, A.; van Eijk, R.; Calvo, A.; Mazzini, L.; et al. C9orf72 Expansion Differentially Affects Males with Spinal Onset Amyotrophic Lateral Sclerosis. J. Neurol. Neurosurg. Psychiatry
**2017**, 88, 281. [Google Scholar] [CrossRef] [PubMed][Green Version] - Atsuta, N.; Watanabe, H.; Ito, M.; Tanaka, F.; Tamakoshi, A.; Nakano, I.; Aoki, M.; Tsuji, S.; Yuasa, T.; Takano, H.; et al. Age at Onset Influences on Wide-Ranged Clinical Features of Sporadic Amyotrophic Lateral Sclerosis. J. Neurol. Sci.
**2009**, 276, 163–169. [Google Scholar] [CrossRef] [PubMed] - Chiò, A.; Calvo, A.; Moglia, C.; Mazzini, L.; Mora, G.; PARALS Study Group. Phenotypic Heterogeneity of Amyotrophic Lateral Sclerosis: A Population Based Study. J. Neurol. Neurosurg. Psychiatry
**2011**, 82, 740–746. [Google Scholar] [CrossRef] [PubMed] - Connolly, O.; Le Gall, L.; McCluskey, G.; Donaghy, C.G.; Duddy, W.J.; Duguez, S. A Systematic Review of Genotype–Phenotype Correlation across Cohorts Having Causal Mutations of Different Genes in ALS. J. Pers. Med.
**2020**, 10, 58. [Google Scholar] [CrossRef] - van Es, M.A.; Hardiman, O.; Chio, A.; Al-Chalabi, A.; Pasterkamp, R.J.; Veldink, J.H.; van den Berg, L.H. Amyotrophic Lateral Sclerosis. Lancet
**2017**, 390, 2084–2098. [Google Scholar] [CrossRef] - Nguyen, H.P.; Van Broeckhoven, C.; van der Zee, J. ALS Genes in the Genomic Era and Their Implications for FTD. Trends Genet.
**2018**, 34, 404–423. [Google Scholar] [CrossRef][Green Version] - Andersen, P.M.; Al-Chalabi, A. Clinical Genetics of Amyotrophic Lateral Sclerosis: What Do We Really Know? Nat. Rev. Neurol.
**2011**, 7, 603–615. [Google Scholar] [CrossRef] - Fratello, M.; Caiazzo, G.; Trojsi, F.; Russo, A.; Tedeschi, G.; Tagliaferri, R.; Esposito, F. Multi-View Ensemble Classification of Brain Connectivity Images for Neurodegeneration Type Discrimination. Neuroinform
**2017**, 15, 199–213. [Google Scholar] [CrossRef][Green Version]

**Figure 4.**ROC Analysis Results of Methods; (

**a**) Bayesian Network, (

**b**) Neural Network, (

**c**) Logistic Regression, (

**d**) Naive Bayes, (

**e**) J48, (

**f**) SVM, (

**g**) KStar, (

**h**) kNN.

Feature Name | Feature Value | Freq. | %Value |
---|---|---|---|

SEX | Female | 79 | 38.7 |

Male | 125 | 61.3 | |

AGE | Below 36 | 29 | 14.2 |

Between 36–52 | 70 | 34.3 | |

Between 52–67 | 79 | 38.7 | |

Upper 67 | 26 | 12.7 | |

UMN | No | 129 | 63.2 |

Yes | 75 | 36.8 | |

LMN | No | 178 | 87.3 |

Yes | 26 | 12.7 | |

BULBAR | No | 182 | 89.2 |

Yes | 22 | 10.8 | |

Total Number of Chronic Patience | Five | 1 | 0.5 |

Four | 1 | 0.5 | |

Three | 12 | 5.9 | |

Two | 20 | 9.8 | |

One | 112 | 54.9 | |

None | 58 | 28.4 | |

PARKIN Level (ng/mL) | Upper than 3.74 | 31 | 15.2 |

Between 2.79–3.74 | 17 | 8.3 | |

Between 2.06–2.79 | 36 | 17.6 | |

Between 1.36–2.06 | 52 | 25.5 | |

Lower than 1.36 | 68 | 33.3 | |

Patient type | ALS | 103 | 50.5 |

Control | 42 | 20.6 | |

N-Control | 40 | 19.6 | |

Parkinson | 19 | 9.3 |

Criteria | Formula |
---|---|

Accuracy | ACC = (TP + TN)/(P + N) |

Geometric Mean | GM = sqrt ((TP/(TP + FN)) × (TN/(TN + FP))) |

Error Rate | EER = (FP + FN)/(TP + TN + FP + FN) |

Precision | PREC = TP/(TP + FP) |

Sensitivity | SENS = TP/(TP + FN) |

Specificity | SPEC = TN/(FP + TN) |

F-Measure | F-Measure = 2 × TP/(2 × TP + FP + FN) |

Matthews Correlation Coefficient | MCC = TP × TN − FP×FN/sqrt((TP + FP) × (TP + FN) × (TN + FP) × (TN + FN)) |

Youden’s index | YI = TPR + TNR − 1 |

Kappa | Kappa = 2 × (TP × TN – FN × FP) / (TP × FN + TP × FP + 2 × TP × TN + FN^{2} + FN × TN + FP^{2} + FP × TN) |

Overall Kappa | Kappa = (p_{0} − p_{e})/(1 − p_{e}) |

p_{0} = observed accuracy; p_{e} = expected accuracy | |

False Positive Rate | FPR = FP/(FP + TN) |

ACC | GM | ERR | SENS | SPEC | F-M | MCC | YI | Kappa | FPR | ROC | |
---|---|---|---|---|---|---|---|---|---|---|---|

Bayesian Network | 0.887 | 0.882 | 0.113 | 0.887 | 0.976 | 0.887 | 0.862 | 0.863 | 0.828 | 0.024 | 0.970 |

Neural Network | 0.828 | 0.826 | 0.172 | 0.828 | 0.963 | 0.828 | 0.787 | 0.791 | 0.741 | 0.037 | 0.953 |

Logistic Regression | 0.819 | 0.817 | 0.181 | 0.819 | 0.960 | 0.819 | 0.772 | 0.778 | 0.727 | 0.040 | 0.951 |

Naive Bayes | 0.799 | 0.800 | 0.201 | 0.799 | 0.940 | 0.799 | 0.736 | 0.739 | 0.693 | 0.060 | 0.951 |

J48 | 0.804 | 0.804 | 0.196 | 0.804 | 0.958 | 0.804 | 0.752 | 0.762 | 0.705 | 0.042 | 0.930 |

Support Vector Machine (SVM) | 0.828 | 0.826 | 0.172 | 0.828 | 0.962 | 0.828 | 0.784 | 0.790 | 0.741 | 0.038 | 0.916 |

KStar | 0.838 | 0.835 | 0.162 | 0.838 | 0.963 | 0.838 | 0.794 | 0.801 | 0.756 | 0.037 | 0.952 |

k-Nearest Neighbor (k-NN) | 0.809 | 0.808 | 0.191 | 0.809 | 0.958 | 0.809 | 0.756 | 0.766 | 0.715 | 0.042 | 0.943 |

ACC | GM | ERR | PREC | SENS | SPEC | F-M | MCC | YI | Kappa | ||
---|---|---|---|---|---|---|---|---|---|---|---|

ALS | Bayesian Network | 1.000 | 1.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |

Neural Network | 0.985 | 0.985 | 0.015 | 1.000 | 0.971 | 1.000 | 0.985 | 0.971 | 0.971 | 0.971 | |

Logistic Regression | 0.975 | 0.975 | 0.025 | 1.000 | 0.951 | 1.000 | 0.975 | 0.952 | 0.951 | 0.951 | |

Naive Bayes | 0.971 | 0.970 | 0.029 | 0.962 | 0.981 | 0.960 | 0.971 | 0.941 | 0.941 | 0.941 | |

J48 | 0.980 | 0.980 | 0.020 | 1.000 | 0.961 | 1.000 | 0.980 | 0.962 | 0.961 | 0.961 | |

SVM | 0.980 | 0.980 | 0.020 | 1.000 | 0.961 | 1.000 | 0.980 | 0.962 | 0.961 | 0.961 | |

Kstar | 0.975 | 0.976 | 0.025 | 0.990 | 0.961 | 0.990 | 0.975 | 0.951 | 0.951 | 0.951 | |

k-NN | 0.956 | 0.956 | 0.044 | 0.990 | 0.922 | 0.990 | 0.955 | 0.914 | 0.912 | 0.912 | |

Control | Bayesian Network | 0.917 | 0.874 | 0.083 | 0.791 | 0.810 | 0.944 | 0.800 | 0.747 | 0.754 | 0.747 |

Neural Network | 0.882 | 0.813 | 0.118 | 0.714 | 0.714 | 0.926 | 0.714 | 0.640 | 0.640 | 0.640 | |

Logistic Regression | 0.868 | 0.845 | 0.132 | 0.642 | 0.810 | 0.883 | 0.716 | 0.638 | 0.692 | 0.631 | |

Naive Bayes | 0.882 | 0.854 | 0.118 | 0.680 | 0.810 | 0.901 | 0.739 | 0.668 | 0.711 | 0.664 | |

J48 | 0.887 | 0.902 | 0.113 | 0.661 | 0.929 | 0.877 | 0.772 | 0.718 | 0.805 | 0.700 | |

SVM | 0.892 | 0.897 | 0.108 | 0.679 | 0.905 | 0.889 | 0.776 | 0.719 | 0.794 | 0.706 | |

KStar | 0.907 | 0.888 | 0.093 | 0.735 | 0.857 | 0.920 | 0.791 | 0.735 | 0.777 | 0.732 | |

k-NN | 0.912 | 0.891 | 0.088 | 0.750 | 0.857 | 0.926 | 0.800 | 0.746 | 0.783 | 0.744 | |

Neurological Control | Bayesian Network | 0.902 | 0.816 | 0.098 | 0.778 | 0.700 | 0.951 | 0.737 | 0.678 | 0.651 | 0.677 |

Neural Network | 0.848 | 0.738 | 0.152 | 0.615 | 0.600 | 0.909 | 0.608 | 0.513 | 0.509 | 0.513 | |

Logistic Regression | 0.848 | 0.668 | 0.152 | 0.655 | 0.475 | 0.939 | 0.551 | 0.471 | 0.414 | 0.462 | |

Naive Bayes | 0.809 | 0.568 | 0.191 | 0.519 | 0.350 | 0.921 | 0.418 | 0.317 | 0.271 | 0.309 | |

J48 | 0.819 | 0.532 | 0.181 | 0.571 | 0.300 | 0.945 | 0.393 | 0.320 | 0.245 | 0.299 | |

SVM | 0.843 | 0.650 | 0.157 | 0.643 | 0.450 | 0.939 | 0.529 | 0.449 | 0.389 | 0.439 | |

KStar | 0.853 | 0.670 | 0.147 | 0.679 | 0.475 | 0.945 | 0.559 | 0.485 | 0.420 | 0.474 | |

k-NN | 0.833 | 0.661 | 0.167 | 0.594 | 0.475 | 0.921 | 0.528 | 0.432 | 0.396 | 0.428 | |

Parkinson | Bayesian Network | 0.956 | 0.903 | 0.044 | 0.727 | 0.842 | 0.968 | 0.780 | 0.759 | 0.810 | 0.756 |

Neural Network | 0.941 | 0.869 | 0.059 | 0.652 | 0.789 | 0.957 | 0.714 | 0.686 | 0.746 | 0.682 | |

Logistic Regression | 0.946 | 0.898 | 0.054 | 0.667 | 0.842 | 0.957 | 0.744 | 0.721 | 0.799 | 0.715 | |

Naive Bayes | 0.936 | 0.840 | 0.064 | 0.636 | 0.737 | 0.957 | 0.683 | 0.650 | 0.694 | 0.648 | |

J48 | 0.922 | 0.832 | 0.078 | 0.560 | 0.737 | 0.941 | 0.636 | 0.600 | 0.677 | 0.593 | |

SVM | 0.941 | 0.842 | 0.059 | 0.667 | 0.737 | 0.962 | 0.700 | 0.669 | 0.699 | 0.667 | |

KStar | 0.941 | 0.920 | 0.059 | 0.630 | 0.895 | 0.946 | 0.739 | 0.721 | 0.841 | 0.707 | |

k-NN | 0.917 | 0.857 | 0.083 | 0.536 | 0.789 | 0.930 | 0.638 | 0.607 | 0.719 | 0.593 |

Target Node (s) | Target Value | Evidence (Patient Type) | |||
---|---|---|---|---|---|

ALS | Control | N-Control | Parkinson | ||

None | None | 0.340 | 0.252 | 0.257 | 0.151 |

AGE | Below 36 | 0.119 | 0.249 | 0.106 | 0.078 |

Between 36–52 | 0.333 | 0.419 | 0.298 | 0.316 | |

Between 52–67 | 0.437 | 0.232 | 0.449 | 0.430 | |

Upper 67 | 0.111 | 0.100 | 0.148 | 0.175 | |

SEX | Female | 0.382 | 0.342 | 0.401 | 0.452 |

Male | 0.618 | 0.658 | 0.599 | 0.548 | |

PARKIN Level (ng/mL) | Upper than 3.74 | 0.244 | 0.107 | 0.098 | 0.114 |

Between 2.79–3.74 | 0.078 | 0.072 | 0.100 | 0.084 | |

Between 2.06–2.79 | 0.192 | 0.200 | 0.159 | 0.131 | |

Between 1.36–2.06 | 0.238 | 0.279 | 0.340 | 0.107 | |

Lower than 1.36 | 0.247 | 0.343 | 0.303 | 0.563 | |

UMN | No | 0.273 | 0.840 | 0.844 | 0.733 |

Yes | 0.727 | 0.160 | 0.156 | 0.267 | |

LMN | No | 0.822 | 0.912 | 0.913 | 0.852 |

Yes | 0.178 | 0.088 | 0.087 | 0.148 | |

BULBAR | No | 0.853 | 0.924 | 0.925 | 0.872 |

Yes | 0.147 | 0.076 | 0.075 | 0.128 | |

Total Number of Patience | None | 0.040 | 0.696 | 0.288 | 0.075 |

One | 0.704 | 0.189 | 0.595 | 0.731 | |

Two | 0.140 | 0.060 | 0.059 | 0.101 | |

Three | 0.103 | 0.042 | 0.045 | 0.070 | |

Four | 0.008 | 0.008 | 0.007 | 0.013 | |

Five | 0.006 | 0.006 | 0.006 | 0.010 |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Karaboga, H.A.; Gunel, A.; Korkut, S.V.; Demir, I.; Celik, R. Bayesian Network as a Decision Tool for Predicting ALS Disease. *Brain Sci.* **2021**, *11*, 150.
https://doi.org/10.3390/brainsci11020150

**AMA Style**

Karaboga HA, Gunel A, Korkut SV, Demir I, Celik R. Bayesian Network as a Decision Tool for Predicting ALS Disease. *Brain Sciences*. 2021; 11(2):150.
https://doi.org/10.3390/brainsci11020150

**Chicago/Turabian Style**

Karaboga, Hasan Aykut, Aslihan Gunel, Senay Vural Korkut, Ibrahim Demir, and Resit Celik. 2021. "Bayesian Network as a Decision Tool for Predicting ALS Disease" *Brain Sciences* 11, no. 2: 150.
https://doi.org/10.3390/brainsci11020150