WITHDRAWN: Improving the Performance of Diagnosing Chronic obstructive Lung Disease Using Outlier Detection with Decision Tree Algorithm

Abstract

Chronic Obstructive Lung Disease (COLD) is a continual inflammatory lung disorder that causes blocked airflow from the lungs. It is also called Chronic Obstructive Pulmonary Disease (COPD.) It is responsible for a variety of different conditions such as chronic bronchitis (breathing passages in the lungs) and emphysema (air sacs in the lungs). The right diagnosis of sickness is one of the foremost important complications in medicine. Hence, computerized analysis of diagnosing COPD has been developed to decrease the time and reduce the demise rate. A classification algorithm is one of the computerized techniques which is utilized for the diagnosis of COPD. In this research work, we proposed a hybrid algorithm called ODWDT (Outlier Detection with Decision Tree) algorithm. It comprises two phases; in the first phase, the thoracic data set is preprocessed using Inter Quartile Range (IQR). In the second phase, the COPD patients are diagnosed and classified by using the J48 decision tree classifier. The thoracic surgery dataset can be used to assess samples that might be risk factors for lung cancer which is taken from the UCI Machine learning repository. Experimental results showed that the proposed method achieved 95% classification accuracyin diagnosing chronic obstructive lung disease patients. The outcome of this research work is to diagnose patients with better classification accuracy so that we can be planned for the next treatment. Results proved that the proposed ODWDT algorithm performs better in the diagnosis of COPD.

On the K-Means Clustering Model for Performance Enhancement of Port State Control

“…The widely used ship risk profile (SRP) analysis considers ship flag, recognized organization (RO), and company since they are crucial to ship management, operation, and maintenance [2]. In return, a vessel's performance in PSC inspections affects its flag state, RO, and company reputation, as well as its performance in PSC memorandum of understanding (MoU) assessments [3,4]. In this case, it is reasonable to conclude that, all other things being equal, if the performance of the flag state/RO/company gets worse, the ship should be estimated to perform worse in the PSC inspection (e.g., more deficiencies and higher probability detection) [5].…”