Analysis of Idiopathic Pulmonary Fibrosis through Machine Learning Techniques

Abstract

Few diseases are hard to detect and life-threatening as well, and Pulmonary Fibrosis (PF) is one of them. PF is a chronic disorder that leads to progressive scarring of the lungs, and we can say that PF is Idiopathic Pulmonary Fibrosis (IPF) because the cause of the disease is unknown. 50,000 fresh cases per year are diagnosed with PF, which is likely to increase. With machine learning and deep learning, we can predict the lung function decline of a patient suffering from IPF. This prediction will improve the medication process and will increase the longevity of the patient. Early detection of IPF is crucial as it increases the morbidity and mortality rate and healthcare costs. We have predicted IPF in the early stages using forced vital capacity (FVC) records of different patients. FVC is the amount of air that we can exhale from our lungs after taking a deep breath. We have created a Multiple-Quantile Regression model to detect a decline in lung function using CNN. With this approach, the cross-validation accuracy of prediction is 92 percent.