POPD Disease Diagnosing and Predictions Using Data Mining Algorithms
R.Jeena1, P.Sarasu2
1Mrs. R. Jeena, Research Scholar, Department of CSE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, (Tamil Nadu), India.
2Dr. P. Sarasu, Professor, Department of CSE, Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, (Tamil Nadu), India.
Manuscript received on 22 April 2019 | Revised Manuscript received on 01 May 2019 | Manuscript Published on 05 May 2019 | PP: 258-262 | Volume-8 Issue-2S2, May 2019 | Retrieval Number: B10540182S219/19©BEIESP
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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: Data Mining is employed to seek out hidden pattern from a vast database. In data mining, machine learning is primarily act as research and from complex patterns which find decisions based on data. Persistent Obstructive Pulmonary Disease (POPD) is an important term accustomed describes progressive respiratory organ diseases as well as respiratory illness, bronchitis, and refractory (non-reversible) respiratory illness. This illness is characterized by increasing shortness of breath. These days Persistent Obstructive Pulmonary Disease (POPD) is one amongst the foremost causes of death within the developing countries. POPD is the main causes of lung cancer. By classification, common thoracic surgery includes information, procedural skill and decision to diagnose and treat diseases of the lungs. In this paper the data classification is Thoracic Surgery (Lung Cancer) patients’ data set which includes 470 instances with 14 attributes is collected respectively. Data mining algorithms plays vital role in disease diagnosing and prediction. Among them we cannot access all DM algorithms. So, in this paper presents, chosen best one algorithm among the Standard DM algorithms for reduce the search and time complexity.
Keywords: Lung Tumor, Thoracic Surgery, Diagnosis, Naive Bayes, Random Forest, OneR, PART, Decision Stump, J48.
Scope of the Article: Data Mining