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@ARTICLE{Panda_INFORMATICA_2020,
         author = {Panda, Debjani and Dash, Satya Ranjan and Ray, Ratula and Parida, Shantipriya},
       projects = {Idiap},
          month = dec,
          title = {Predicting the Causal Effect Relationship Between COPD and Cardio Vascular Diseases},
        journal = {Informatica},
         volume = {44},
         number = {4},
           year = {2020},
            url = {http://www.informatica.si/index.php/informatica/article/view/3088},
            doi = {https://doi.org/10.31449/inf.v44i4.3088},
       abstract = {Coronary Obstructive Pulmonary Disease (COPD) is one of the critical factors that are affecting the health of the population worldwide and in most cases affects the patient with cardiovascular diseases and their mortality. The onset of COPD in a patient in most of the cases affects him/her with cardiovascular disease and the management of the disease becomes more complex for medical practitioners to handle. The factorsaffecting COPD and cardiovascular disease in patients are most of the time, concurrent, and are responsiblefor their mortality.  The list of factors and their underlying causes have been identified by experts and are treated with utmost importance before the patient suffers from an emergency condition and its management becomes even more difficult.This paper discusses the need to study COPD and the factors affecting it to avoid cardiovascular deaths.The dataset used for the study is a novel one and has been collected from a Government Medical College,for study and experimentation. Classification methods like Decision Trees, Random Forest (RF), LogisticRegression (LR), SVM (Support Vector Machine), KNN (K-Nearest Neighbours), and Na{\"{\i}}ve Bayes havebeen used and Random Forests have given the best results with 87.5\% accuracy.  The importance of thepaper is in the attempt to infer important links between the associated features to predict COPD. To thebest of our knowledge, such an attempt to infer the interrelation between cardiac disease and COPD usingMachine Learning classifiers has not been made yet. The paper focuses on determining the importantcorrelation between the associated features of COPD and compare different supervised classifiers to checktheir prediction performance.  Coronary Pulmonate, Age, and Smoking have shown a strong correlationwith the presence of COPD and the performance analyses of the classifiers have been shown using the ROC (Receiver Operating Characteristic) curve.}
}