Application Of Data Mining Tools In Disease Risk Factors And Treatment Methods

Objective: This paper makes the co-occurrence matrix of risk factors andtreatment of disease, and as a data object, to make K-Means analysis by using R andWeka. And then uses discriminant analysis in SPSS, to make the comparativeevaluation the suitability of R and Weka. Face to four data mining methods for doinga comparative analysis, including its strengths, weaknesses and scope of application.Proposing the feasibility of collaborative applications, and help researchers selectdata mining.Methods: Retrieval the relevant literature that the risk factors and treatmentmethods of Amyotrophic Lateral Sclerosis in Web of ScienceTM core databasebefore2015. Download the whole message, to do the data preprocessing, filteringhigh frequency keyword. Then retrieval the combination of the keywords with“Amyotrophic Lateral Sclerosis OR ALS” in the whole Web of Science database.Make co-occurrence matrix of risk factors and treatment as a data mining objects.Take data mining theory as theoretical basis, data mining methods as a means, R andWeka as platform and environment, to do data mining. Compare and evaluation theconsequence of K-Means analysis for the risk factors and treatment of ALSco-occurrence matrix in R and Weka by discriminant analysis in SPSS. And evaluatethe more applicable one.Results:(1) According to the principles and characteristics of the four types ofdata mining methods, and the application status in the medical field, to do acomparative analysis, and analysis of the feasibility of the scope and collaborativeapplications in the medical field.(2) Comparing the advantages and weaknesses ofthe four data mining methods, be clear about the data mining methods can be used tomedical data mining. Choose the appropriate data mining methods when aim at the different characteristics of data.(3) Through the co-occurrence matrix of risk factorsand treatment of amyotrophic lateral sclerosis, finding the co-occurrence betweentwo keywords and the strong correlation, at the same time can provide the prompt forbasic experimental and clinical research.(4) Through analyzing the co-occurrencematrix of risk factors and treatment of amyotrophic lateral sclerosis by K-Means,findthat associations within calss, may have relevance in the mechanism of action. And itcan suggest the associations in class that have not found, providing the reference andprediction.Conclusion:(1) Comparing and analyzing the data mining methods to helpchoose the appropriate methods.To a certain extent data mining can be consideredcollaborative applications, strengthening effect or make up for deficiencies.(2) Co-occurrence matrixes of risk factors and treatment can help discover theirassociation strength and the research status.(3) Classification results of this study byR and Weka are different, using SPSS to do discriminant analysis, then find R moreapplicability than Weka for analyzing co-occurrence matrix of disease risk factorsand treatment methods by K-Means.