Optimizing COPD Management: Machine Learning Solutions for Early Detection and Data Privacy

Worldwide, Chronic obstructive pulmonary disease (COPD) is one of the reasons for the high number of deaths. Patients who have COPD face many struggles and lose their quality of life. Machine learning has the ability to enhance the life of the patient through the proper treatments by doctors in the early stages. Machine Learning technology has gained more attention in the medical sector, its main objective is to enhance the accuracy and speed of the physicians. Machine learning-based models help to find the disease at its early stage. This work imparts an overall analysis of machine learning utilization in disease diagnosis and data privacy-preserving. In this work, COPD diagnosis by the machine learning method is overall analyzed. This imparts utilization of two machine learning methods in identifying COPD and its severity level namely Support Vector Machine (SVM) and Random Forest (RF). Both techniques have high-level impacts in medical impacts by lower training time. This work examines some pre-processing steps in obtaining quality input.