Ensemble ML COPD Diagnosis via Exhaled CO Analysis
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Solution Overview
Problem
Current methods for diagnosing Chronic Obstructive Pulmonary Disease (COPD) are costly and not widely accessible, especially in rural areas, and do not effectively utilize demographic data and clinical features such as exhaled carbon monoxide levels.
Innovation Solution
A low-cost, web-based system using an ensemble Machine Learning model that combines Decision Tree, Random Forest, and Gradient Boosting algorithms to classify patients as COPD-positive or COPD-negative based on demographic data and clinical features like FEV, MWT, and exhaled CO levels, utilizing a handheld CO analyzer for data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional diagnostic methods are used for COPD detection, then diagnostic accuracy can be maintained, but the cost becomes excessively high and accessibility is poor
Solution Approach 1:
The patent replaces expensive traditional diagnostic equipment with a low-cost handheld carbon monoxide analyzer that uses affordable sensors and portable computing devices. The system uses disposable or reusable low-cost sensors to measure exhaled CO levels, eliminating the need for expensive hospital-based spirometry equipment while maintaining diagnostic reliability through accurate CO measurement.
Solution Approach 2:
The patent substitutes complex mechanical diagnostic systems (spirometry equipment, pulmonary function tests) with a simplified electronic measurement system that directly measures exhaled carbon monoxide levels. This replacement uses electronic sensors and software-based analysis instead of mechanical breathing apparatus, significantly reducing cost while preserving diagnostic accuracy.
2Measurement precision
If comprehensive clinical tests are conducted for accurate COPD diagnosis, then diagnostic precision improves, but the complexity of the diagnostic process increases
Solution Approach 1:
The patent extracts the specific diagnostic information needed for COPD detection (exhaled CO levels) from the comprehensive set of clinical tests. Instead of requiring multiple complex tests, the system isolates and measures the single most informative parameter - carbon monoxide concentration in exhaled breath - which provides sufficient diagnostic precision with minimal procedural complexity.
Solution Approach 2:
The handheld device combines multiple functions into a single portable unit: CO sensing, data processing, diagnostic algorithm execution, and result display. This multi-functional integration eliminates the need for separate equipment for each diagnostic step, reducing overall system complexity while maintaining high measurement precision through coordinated sensor and software operations.
3Reliability
If advanced diagnostic equipment is deployed, then detection reliability improves, but the accessibility to rural and resource-constrained areas deteriorates
Solution Approach 1:
The patent segments the diagnostic capability from centralized hospital equipment and distributes it through portable handheld devices that can be deployed to remote clinics and rural areas. This segmentation allows reliable COPD detection to occur at the point of care without requiring patients to travel to specialized centers, significantly improving accessibility while maintaining detection reliability through portable sensing technology.
Solution Approach 2:
The patent introduces a portable handheld device as an intermediary between patients in remote areas and diagnostic capabilities. This intermediary device bridges the gap by bringing hospital-grade diagnostic functionality to rural locations, enabling reliable detection without requiring infrastructure for advanced equipment while improving accessibility to underserved populations.
Data Source
AI summary
The present invention is related to a simple, low-cost method for the early diagnosis of COPD in patients using an ensemble ML model residing on cloud or server. A web-based application accessed on a smart phone or a computing device through internet facilitates the entry of patient characteristics such as age, gender, smoking habits, hypertension, pack history, etc. and clinical features such as FEV, MWT and CO levels in the exhaled breath of patients. The patient characteristics is used with the ensemble ML model to predict the severity of COPD disease. The web application also enables sending the results to a receiver via email or text message for further action.