COPD CLASSIFICATION USING MACHINE LEARNING ALGORITHMS
Objective
This paper aims to leverage Support Vector Machines (SVM) for classifying COPD severity levels, enhancing early diagnosis and improving patient outcomes through efficient AI-based methodologies.
Abstract
Numerous afflictions significantly impact human well-being, exerting influence on both the duration and quality of life in various ways. Notably, respiratory conditions such as Chronic Obstructive Pulmonary Disease (COPD), lung cancer, pneumonia, and asthma emerge as substantial health challenges and prominent causes of mortality globally, spanning both developed and developing nations. Medical experts emphasize the critical role of early disease diagnosis and classification, as it significantly enhances the likelihood of successful patient outcomes. In this context, Artificial Intelligence (AI) algorithms and Expert Systems have proven effective in addressing diverse challenges, particularly within the medical field. Their advantages include the expedited diagnosis and classification process, time efficiency, and heightened overall efficacy. Consequently, Artificial Neural Networks stand out as a promising tool for COPD classification, this paper explores the utilization of Support Vector Machines (SVM) methodology for classifying COPD severity levels.
Key Words: Chronic Obstructive Pulmonary Disease (COPD), Machine Learning Algorithms, Support Vector Machine (SVM), COPD Classification.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Software: Matlab 2020a or above
Hardware:
Operating Systems:
- Windows 10
- Windows 7 Service Pack 1
- Windows Server 2019
- Windows Server 2016
Processors:
Minimum: Any Intel or AMD x86-64 processor
Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support
Disk:
Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation
Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space
RAM:
Minimum: 4 GB
Recommended: 8 GB
Learning Outcomes
· Introduction to Matlab
· What is EISPACK & LINPACK
· How to start with MATLAB
· About Matlab language
· Matlab coding skills
· About tools & libraries
· Application Program Interface in Matlab
· About Matlab desktop
· How to use Matlab editor to create M-Files
· Features of Matlab
· Basics on Matlab
· What is an Image/pixel?
· About image formats
· Introduction to Image Processing
· How digital image is formed
· Importing the image via image acquisition tools
· Analyzing and manipulation of image.
· Phases of image processing:
o Acquisition
o Image enhancement
o Image restoration
o Color image processing
o Image compression
o Morphological processing
o Segmentation etc.,
· How to extend our work to another real time applications
· Project development Skills
o Problem analyzing skills
o Problem solving skills
o Creativity and imaginary skills
o Programming skills
o Deployment
o Testing skills
o Debugging skills
o Project presentation skills
o Thesis writing skills
Paper Publishing
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