Symptoms Analysis Based Chronic Obstructive Pulmonary Disease Prediction In Bangladesh Using Machine Learning Approach

Chronic Obstructive Pulmonary Disease (COPD) is a set of lung diseases in which the airways constrict, making it difficult to expel air from the lungs. COPD is caused by longterm lung damage from breathing a potential hazard, mainly frequently cigarette smoking, as well as smoke from other sources and air pollution. Also, some rural areas where people are exposed to dust, flames, and chemicals can contribute to the onset of COPD. COPD is supposed to after 40 years. Some major problems getting short of breath, having cough, wheezing, also produced more sputum, respiration rate abnormal, sometimes getting restless. In Bangladesh, day by day patients with COPD are increasing. So, in this case, need to analyze the symptoms of COPD and finds why it is increasing day by day. In this research, we have taken 101 COPD patients data from the hospital which are in two classes. In the medical sector, work for COPD is rare in Bangladesh. Our goal is to find the most appropriate machine learning approach for COPD prediction which is both computationally efficient and accurate. Decision Tree and Logistic Regression outperformed all other machine learning algorithms, according to the findings in our study.