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AI InsightThe results also showed that there was no relationship between demographic and clinical features such as gender, occupation, age group, and concomitant disease between groups or within groups.
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Abstract
Introduction: Amyotrophic lateral sclerosis (ALS) is a motor neuron disease that affects nerve cells in the brain and spinal cord, controlling voluntary muscle movement. Data mining is a discipline that provides meaningful conclusions from databases or implicit data. In this study, we examine the relationship between the clinical and demographic characteristics of ALS patients and a control group, using data mining techniques. Methods: In the study, data belonging to 235 patients diagnosed with ALS and a control group of 117 people consisting of relatives of ALS patients were used. The dataset contains 121 features that include clinical and demographic information for each patient. The patient group and the control group were examined together and separately to examine the relationship between the features. In the study the data mining methods of classification and clustering were used on R and WEKA software packages. Results: There were no significant differences between ALS patients and the control group in terms of environmental factors such as location, gender, smoking, exercise status, and clinical factors such as genetics, ALS involvement, course of the disease, disease in the family. The results also showed that there was no relationship between demographic and clinical features such as gender, occupation, age group, and concomitant disease between groups or within groups.