ANALYZING CLINICAL SYMPTOMS IN MULTIPLE SCLEROSIS USING DATA MINING

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RAEISI ZAHRA; RAMEZANNEZAD PANTEA; AHMADZADEH MARZIEH; TARAHOMI SHAHRAM

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Journal: Tehran University Medical Journal Year:2017 | Volume:75 | Issue:1 Page(s): 39-48

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Title

ANALYZING CLINICAL SYMPTOMS IN MULTIPLE SCLEROSIS USING DATA MINING

Author(s)

RAEISI ZAHRA | RAMEZANNEZAD PANTEA | AHMADZADEH MARZIEH | TARAHOMI SHAHRAM | Issue Writer Certificate

Keywords

ASSOCIATIONQ2

CLASSIFICATIONQ1

DATA MININGQ2

MULTIPLE SCLEROSISQ1

Abstract

Background: One of the today most common and incurable diseases that is associated with central neural system is ‘MS’ disease. MULTIPLE SCLEROSIS (MS) is a demyelinating disease in which the insulating covers of nerve cells in the brain and spinal cord are damaged. In this disease become apparent a wide spectrum of symptoms such as lose muscles control and their coordination and vision derangement. The goal of this research is to consider to two problems: 1- Recognition of effective clinical symptoms on MS disease and 2- Considering levels of effectiveness of age, sex and education levels factors on MS disease and ASSOCIATION between these factors according to verity of categories of this disease.Methods: DATA MINING science in medicine is worthy of attention with main application in diagnosis, therapy and prognosis, respectively high volume of collected datum. The data that were used in this article are about patients of Chaharmahal and Bakhtiari Province and collected by cure assistance. In this paper CLASSIFICATION and ASSOCIATION methods in software engineering field are used.Classification is a general process related to categorization, the process in which ideas and objects are recognized, differentiated, and understood.ASSOCIATION rules are created by analyzing data for frequent if/then patterns and using the criteria support and confidence to identify the most important relationships.Results: In consideration of first problem in this paper, concluded vision-clinical symptoms are the most effective symptoms and in consideration of second problem, concluded that from 584 records, women affected four times more than men. In other word 70% of MS patients with high graduate are in relapsing-remitting category and 62.5% of MS patients are 20-40 years old.Conclusion: Some of symptoms are quite temporary and transitory and are ignored by people. Awareness of clinical-symptoms prevalence manner can be warning for people before starting critical cycle of illness. This would cause early diagnosis, effective therapy and even prevention of disease progress, respectively to MS chronicity.

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APA: Copy

RAEISI, ZAHRA, RAMEZANNEZAD, PANTEA, AHMADZADEH, MARZIEH, & TARAHOMI, SHAHRAM. (2017). ANALYZING CLINICAL SYMPTOMS IN MULTIPLE SCLEROSIS USING DATA MINING. TEHRAN UNIVERSITY MEDICAL JOURNAL (TUMJ), 75(1), 39-48. SID. https://sid.ir/paper/38065/en

Vancouver: Copy

RAEISI ZAHRA, RAMEZANNEZAD PANTEA, AHMADZADEH MARZIEH, TARAHOMI SHAHRAM. ANALYZING CLINICAL SYMPTOMS IN MULTIPLE SCLEROSIS USING DATA MINING. TEHRAN UNIVERSITY MEDICAL JOURNAL (TUMJ)[Internet]. 2017;75(1):39-48. Available from: https://sid.ir/paper/38065/en

IEEE: Copy

ZAHRA RAEISI, PANTEA RAMEZANNEZAD, MARZIEH AHMADZADEH, and SHAHRAM TARAHOMI, “ANALYZING CLINICAL SYMPTOMS IN MULTIPLE SCLEROSIS USING DATA MINING,” TEHRAN UNIVERSITY MEDICAL JOURNAL (TUMJ), vol. 75, no. 1, pp. 39–48, 2017, [Online]. Available: https://sid.ir/paper/38065/en

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