CLDC: Efficient Classification of Medical Data Using Class Level Disease Convergence Divergence Measure
1K.Ananthajothi*, Assistant Professor, Department of Computer Science and Engineering, Misrimal Navajee Munoth Jain Engineering College, Chennai, India.
2M.Subramaniam, Professor, S.A. Engineering College, Chennai, India.
Manuscript received on 02 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 August 2019 | PP: 2256-2262 | Volume-8 Issue-10, August 2019 | Retrieval Number: J11230881019/2019©BEIESP | DOI: 10.35940/ijitee.J1123.0881019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The problem of medical data classification is analyzed and the methods of classification are reviewed in various aspects. However, the efficiency of classification algorithms is still under question. With the motivation to leverage the classification performance, a Class Level disease Convergence and Divergence (CLDC) measure based algorithm is presented in this paper. For any dimension of medical data, it convergence or divergence indicates the support for the disease class. Initially, the data set has been preprocessed to remove the noisy data points. Further, the method estimates disease convergence/divergence measure on different dimensions. The convergence measure is computed based on the frequency of dimensional match where the divergence is estimated based on the dimensional match of other classes. Based on the measures a disease support factor is estimated. The value of disease support has been used to classify the data point and improves the classification performance.
Keywords: High Dimensional Clustering, Classification, Medical Data, Convergence, Divergence, Disease Prediction
Scope of the Article: Classification