Abstract
Multiple sclerosis (MS) is a relatively common neurodegenerative illness that frequently causes a large level of disability in patients. While its cause is not fully understood, it is likely due to a combination of genetic and environmental factors. Diagnosis of multiple sclerosis through a simple clinical examination might be challenging as the evolution of the illness varies significantly from patient to patient, with some patients experiencing long periods of remission. In this regard, having a quick and inexpensive tool to help identify the illness, such as DNA CpG (cytosine-phosphate-guanine) methylation, might be useful. In this paper, a technique is presented, based on the concept of Shannon Entropy, to select CpGs as inputs for non-linear classification algorithms. It will be shown that this approach generates accurate classifications that are a statistically significant improvement over using all the data available or randomly selecting the same number of CpGs. The analysis controlled for factors such as age, gender and smoking status of the patient. This approach managed to reduce the number of CpGs used while at the same time significantly increasing the accuracy.
Highlights
Multiple sclerosis (MS) is a chronic autoimmune illness affecting the brain and spinal cord associated with various degrees of disability
The direct approach likely generates poor classifications due to the issue of local minima, which is likely improved by the introduced Shannon Entropy filtering
All the results shown below refer to the testing dataset results
Summary
Multiple sclerosis (MS) is a chronic autoimmune illness affecting the brain and spinal cord associated with various degrees of disability. In MS, the immune system of the patient attacks the axons, the myelin cover; see Figure 1 for a graphical illustration [1]. Inflammation is highlighted by some researchers as one of the drivers of neurodegeneration in MS [2–4]. The evolution of the illness varies greatly from patient to patient, with some individuals experiencing long periods of remissions due to mechanisms that are not yet well understood. The usual manifestation age of the illness is from 20 to 45 years old, but it can occasionally manifest at younger ages, even in children [5]. The causes of MS remain unclear, with a complex underlying combination of genetic and environmental factors the most likely cause [6–10]
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