A HYBRID MODEL FOR PREDICTING MALARIA USING DATA MINING TECHNIQUES
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- Recommended for : Student Researchers
- NGN 3000
ABSTRACT
Data mining is used in extracting rules to predict certain information in many areas of Information Technology, medical science, biology, education, and human resources. Data mining can be applied on medical data to foresee novel, useful and potential knowledge that can save a life, reduce treatment cost, increases diagnostic and prediction accuracy as well as save human resources. Data mining involve several techniques such as anomaly detection, classification, regression, clustering, time series analysis, association rule, and summarization. Classification is the most important application of data mining. In this thesis, we use a classification technique called Naïve Bayes (a supervised learner) to build a hybrid framework for classifying and predicting the status of only malaria and their complications in a suspect patient using their clinical presentation. For the purpose of this study, we considered the parameter: fever, headache, nausea, vomiting, respiratory distress, convulsion, and coma as the main distinct clinical symptom. This method has the relative advantage of easy to construct, can classify categorical data, and occurrences of an event (attributes) are independent, and work better on high dimensional data. The framework developed was divided into two phases Classification Phase 1, Classification Phase 2 and is implemented using Java built on Weka library version 3.8.0. The framework was trained using data acquired from hospital and tested for performance accuracy using Receiver Operating Characteristic (ROC) and Confusion Matrix (CM). The results demonstrated that the system predicted accurately with performance accuracy of 90%, 98% on confusion matrix and 92%, 99% on ROC-Area under Curve (ROC-AUC) for Classification Phase 1 and Classification Phase 2 respectively. This means that ROC presented more optimal result than confusion matrix and such system should be useful for rural area where clinician or medical equipment are is not available to assist in predicting malaria is suspected malaria patient.
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