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.

Classroom rules are a fundamental aspect of classroom management and ensuring compliance with established rules is crucial. Previous research has shown that students often pay little attention to the development of classroom rules. This quantitative study aims to investigate the expectations that students have concerning classroom rules. To this end, a 4-point Likert scale questionnaire consisting of 30 items was administered to 356 secondary school students. The Bayesian Search method and expert opinion were used to obtain a Bayesian Network model. The findings of the study indicate that students expect rules to be determined at the beginning of the academic year, wish to be involved in the determination process, and prefer minimal changes to the rules. They also expect a limited number of rules and reinforcement from teachers for displaying desirable behavior. Additionally, the study found that students are more likely to adhere to classroom rules in a clean and uncrowded environment, and prefer that their parents are not informed about these rules. The results also suggest that increased adherence to classroom rules leads to increased class inclusion, while decreased adherence results in decreased class inclusion. Furthermore, the study found that adoption of classroom rules leads to increased in-class cohesion, while non-adoption results in decreased cohesion. These findings contribute to the existing body of knowledge concerning student expectations of classroom rules.