Early Prediction Of Amyotropic Lateral Sclerosis Using Ml Algorithms And Speech Signal Processing

Abstract

The Developed System For ALS Detection Using Speech Recognition And Machine Learning Effectively Demonstrates How Artificial Intelligence Can Be Leveraged To Support Early Diagnosis Of Neurological Disorders. By Analyzing Subtle Variations In Speech Patterns Using MFCC-based Feature Extraction And Machine Learning Algorithms, The System Provides A Reliable, Non-invasive, And Cost-efficient Method For Identifying Early Signs Of Amyotrophic Lateral Sclerosis (ALS). The System Continuously Enhances Its Diagnostic Accuracy Through User Feedback And Periodic Model Retraining Using Newly Collected Voice Samples. This Adaptive Learning Approach Ensures That Predictions Remain Consistent And Relevant, Even As More Diverse Data Is Introduced. Moreover, The Integration Of Visualization Modules And Probability-based Outputs Improves The Transparency Of AI Decisions, Helping Users And Healthcare Professionals Better Interpret The System’s Findings. Future Enhancements Aim To Incorporate Advanced Deep Learning Techniques Such As CNNs And RNNs For Improved Pattern Recognition, As Well As Real-time Monitoring And Disease Progression Tracking. Expanding The Dataset To Include Multiple Languages And Dialects Will Also Make The Model More Inclusive And Globally Applicable. Overall, This Project Establishes A Strong Foundation For AI-driven Healthcare Systems That Can Assist In Early Detection, Patient Monitoring, And Decision-making Support For Neurodegenerative Diseases.

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