Prediction of Optimal Algorithm For Diagnosis of Chronic Obstructive Pulmonary Disease

Data analyzing is the process of analyzing the dataset to make inferences from the information available. The main aim is to apply statistical analysis and technologies on data to solve problems. Thus, researchers introduce various algorithms for analysis the data. But the existing algorithms have not achieve the expected outcome. Thus, the proposed work also addresses to the improve the mechanism for analysis the dataset for the prediction of an optimal algorithm for diagnosis of Chronic Obstructive Pulmonary Disease (COPD). Airflow into and out of the lungs is impeded by COPD. Long-term exposure to irritating gases or particles, most typically from cigarette smoke, is frequently the cause. People with COPD have a higher risk of developing heart disease, lung cancer, and a variety of other disorders. Here this work compares various machine learning algorithms for the huge volume of medical data with multiple attributes. The objective is to predict the algorithm which has the highest accuracy. With the help of analytics of the chosen dataset, the above-mentioned models are deployed and compared for the prediction of the algorithm with the highest accuracy rate of 97%.