文章基本信息

标题：A FUZZY LOGIC BASED HYBRID APPROACH FOR DISEASE INTERPRETATION AND PREDICTION
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作者：MR. RAVI AAVULA ; DR. R. BHRAMARAMBA
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2018
卷号：96
期号：16
出版社：Journal of Theoretical and Applied
摘要：Data mining and data exploration in databases are attracting a big quantity of analytics, research, industry, and media attention these days. Despite the growing number of machine-learning algorithms that have been formed, still to implement them and provide the effectiveness and practicality is much desired. However, in order to help the medical experts to suggest a proper and an efficient medical plan by employing the predicted output of the built model, it is significantly needful to determine which attribute-variables have more significance to the final outcome of cancer patients� patterns. This paper presents a novel fuzzy logic based hybrid approach for cancer disease interpretation and prediction. The earlier forecast and location of disease cells can be useful in curing the illness in medical applications. We performed the experiments on Breast Cancer Wisconsin Data Set utilizing our proposed method. Experiment analysis in later section prove the efficiency of our proposed method. Proposed method is computationally more efficient than existing methods and, therefore, suited even for massive sized data sets in the biomedical field.
关键词：Knowledge discovery; Data mining; Machine learning; Medical data; Cancer prognosis.