A Data-Driven Approach to Pre-Operative Evaluation of Lung Cancer Patients

TL;DR

This paper presents a novel, wearable, mobile device for evaluating lung function in lung cancer patients, utilizing machine learning to analyze breath data and predict activity levels, aiming to replace costly traditional tests.

Contribution

It introduces a lightweight, patient-centric device with sensor integration and machine learning analysis for at-home pulmonary function assessment in lung cancer patients.

Findings

Device successfully collects breath data during exercises

Machine learning models can classify activity levels accurately

Potential to replace traditional pulmonary tests with a mobile solution

Abstract

Lung cancer is the number one cause of cancer deaths. Many early stage lung cancer patients have resectable tumors; however, their cardiopulmonary function needs to be properly evaluated before they are deemed operative candidates. Consequently, a subset of such patients is asked to undergo standard pulmonary function tests, such as cardiopulmonary exercise tests (CPET) or stair climbs, to have their pulmonary function evaluated. The standard tests are expensive, labor intensive, and sometimes ineffective due to co-morbidities, such as limited mobility. Recovering patients would benefit greatly from a device that can be worn at home, is simple to use, and is relatively inexpensive. Using advances in information technology, the goal is to design a continuous, inexpensive, mobile and patient-centric mechanism for evaluation of a patient's pulmonary function. A light mobile mask is designed, fitted with CO2, O2, flow volume, and accelerometer sensors and tested on 18 subjects performing 15 minute exercises. The data collected from the device is stored in a cloud service and machine learning algorithms are used to train and predict a user's activity .Several classification techniques are compared - K Nearest Neighbor, Random Forest, Support Vector Machine, Artificial Neural Network, and Naive Bayes. One useful area of interest involves comparing a patient's predicted activity levels, especially using only breath data, to that of a normal person's, using the classification models.