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World Journal of Engineering
Research and Technology

( An ISO 9001:2015 Certified International Journal )

An International Peer Reviewed Journal for Engineering Research and Technology

ISSN 2454-695X

Impact Factor : 5.924

ICV : 79.45

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Indexing

Abstract

ANALYSIS OF MALARIA DIAGNOSIS ON PATIENTS USING DATA MINING CLUSTERING TECHNIQUES

Mani Shanker Chaubey*, Adelaja Adebayo Oluwaseun and Nyaaku Oluwayemisi Esther

ABSTRACT

The research was carried out on the malaria patients with some symptoms on high rate that shows positive +ve result while those with some symptoms on low rate that shows negative -ve result. KNIME data mining tool was used to build a comprehensive work flow model consisting of nodes with their respective functions. Fuzzy c-mean, k-mean and hierarchical clustering nodes were utilized to produce grouped subsets termed clusters from the malaria_result.csv file (training-set). A decision tree level classifier was designed from the patient’s diagnosis of the malaria symptoms. Data Analysis Knowledge Discovery Process for the clustering was also built. The result obtained in this research shows statistical clustering means such as scatter plots, interactive histogram, clustered data table and interactive tables which will be helpful for future observations and predictions of malaria in health care.

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