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Genomic sequence analysis of lung infections using artificial intelligence technique.

R Kumar¹, Fadi Al-Turjman², L Anand³

¹Department of Electronics and Instrumentation Engineering, National Institute of Technology, Chumkedima, Dimapur, Nagaland, 797103, India.

Interdisciplinary Sciences, Computational Life Sciences

|February 9, 2021

Summary

This study introduces a Parallel Support Vector Machine (P-SVM) method for early lung disease detection using genomic data. The AI-driven approach achieves high accuracy in classifying lung illnesses from real-time cloud and IoT data.

Keywords:

Artificial intelligence Genomic sequence Healthcare IoT Lung malignancy Neural computing

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Area of Science:

Artificial Intelligence in Healthcare
Genomic Data Analysis
Machine Learning for Disease Classification

Background:

Advancements in Artificial Intelligence (AI) are transforming healthcare, with deep learning and Support Vector Machines (SVM) playing key roles.
Convolutional Neural Networks (CNNs) are widely used for classification tasks in diseases like lung cancer.

Purpose of the Study:

To develop an optimized classification methodology for lung diseases using genomic sequences.
To enable early detection and control of lung disease progression through an AI-based approach.

Main Methods:

Implementation of a Parallel Support Vector Machine (P-SVM) algorithm for classifying high-dimensional lung disease datasets.
Utilizing the Internet of Things (IoT) and cloud computing for real-time data acquisition.

Main Results:

The developed P-SVM algorithm demonstrated 83% higher accuracy and 88% precision in lung disease classification.
The method proved effective in classifying complex, high-dimensional lung disease datasets.

Conclusions:

The P-SVM approach offers a robust method for the early and accurate classification of lung diseases.
Integration with IoT and cloud platforms enhances the real-time applicability of AI in diagnosing lung conditions.