2017/07/21 by Oleksiy Budilovsky, Budilovsky, Oleksiy, A. Knoesen +6
Medicine · #Chronic Obstructive Pulmonary Disease (COPD) Research #Computers and Society (cs.CY) #FOS: Computer and information sciences #Neural and Evolutionary Computing (cs.NE)
paper · pdf · doi:10.48550/arxiv.1707.08169
openalex publication_date 2017/07/21 · openalex created_date 2022/10/05 · openalex updated_date 2026/07/28
Lung cancer is the number one cause of cancer deaths. Many early stage lung\ncancer patients have resectable tumors; however, their cardiopulmonary function\nneeds to be properly evaluated before they are deemed operative candidates.\nConsequently, a subset of such patients is asked to undergo standard pulmonary\nfunction tests, such as cardiopulmonary exercise tests (CPET) or stair climbs,\nto have their pulmonary function evaluated. The standard tests are expensive,\nlabor intensive, and sometimes ineffective due to co-morbidities, such as\nlimited mobility. Recovering patients would benefit greatly from a device that\ncan be worn at home, is simple to use, and is relatively inexpensive. Using\nadvances in information technology, the goal is to design a continuous,\ninexpensive, mobile and patient-centric mechanism for evaluation of a patient's\npulmonary function. A light mobile mask is designed, fitted with CO2, O2, flow\nvolume, and accelerometer sensors and tested on 18 subjects performing 15\nminute exercises. The data collected from the device is stored in a cloud\nservice and machine learning algorithms are used to train and predict a user's\nactivity .Several classification techniques are compared - K Nearest Neighbor,\nRandom Forest, Support Vector Machine, Artificial Neural Network, and Naive\nBayes. One useful area of interest involves comparing a patient's predicted\nactivity levels, especially using only breath data, to that of a normal\nperson's, using the classification models.\n