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Predicting Lung Disease Severity Evaluation and Comparison of Hybird Decision Tree Algorithm

Affiliations

  • Department of Information Technology, School of Computing Sciences, Vels University, Chennai, India

Abstract


Objective: To focus on classification algorithms to arrive better prediction model for Lung Disease Severity.

Methods/Statistical analysis: In therapeutic analyses, the part of information mining methodologies is being expanded. Especially Classification calculations are exceptionally useful in arranging the information, which is critical for basic leadership prepare for therapeutic experts. In this paper the analysis is done in the WEKA apparatus on the spiro informational index.

Findings: The paper embarks to make relative assessment of classifiers, for example, J48, Random forest and proposed Hybird Decision Tree(HDT) Algorithm with regards to Spiro dataset to amplify genuine positive rate and limit false positive rate of defaulters as opposed to accomplishing just higher grouping exactness utilizing WEKA instrument. The tests comes about appeared in this paper are about grouping exactness, affectability and specificity.

Application/Improvements: The outcomes created on this dataset likewise demonstrate that the productivity and exactness of J48 is superior to anything other choice tree classifiers. J48 develops purge branches, it is the most urgent stride for govern era in J48. In more often than not this approach over fits the preparation cases with boisterous information. The proposed Hybird Decision Tree (HDT) Algorithm demonstrates great exactness in less time.


Keywords

Decision Tree, Pulmonary Function Test Means, Spirometry Data, Hybird Decision Tree Algorithm, J48 Algorithm.

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