Early Prediction of Lung Diseases

Abstract

Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to "learn" from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. Machine learning offers a principle approach for developing sophisticated, automatic, and objective algorithms for analysis of high-dimensional and multimodal biomedical data. Machine Learning plays an important role in medical systems. Earlier identification of diseases, we can be helped to detect earlier and more accurately, which can save many people as well as reduce the pressure on the system. Lung diseases are the one of the leading cause of death. The early identification and prediction of a lung diseases have become a necessity in the research, as it can facilitate the subsequent clinical management of patients. Machine Learning based decision support system provide the contribution to the doctors in their diagnosis decisions. Project considered about the breathing problems of patients as well as Asthma, Chronic Obstructive Pulmonary Disease (COPD), Tuberculosis, Pneumothorax and Lung cancer. Machine Learning and Deep Learning used to process data as well as create models for diagnosing patients. Combining the processing of patient information with data from chest X-rays, using CNN with the well-known pre-trained model, Caps Net network for data this form are the methods used for this project to identify the lung diseases. Initially studied and analyzed the data set, then apply Machine Learning and Deep Learning to predict that the patient has a lung disease or not. Project is a binary classification with input is patient's data (age, gender, chest X-ray images & view position) and output is found what the diseases is or not. The aim of the paper is to detect and diagnose the lung diseases as early as possible which will help the doctor to save the patient's life. This paper describes how lung diseases was predicted and controlled, using Machine Learning.