Development of a Machine Learning Model for Early Detection of Amyotrophic Lateral Sclerosis (ALS) Using Speech and Stability Analysis
Project by Polygence alum Arnav
Project's result
Completed a full-length research paper titled Development of a Machine Learning Model for Early Detection of Amyotrophic Lateral Sclerosis (ALS) Using Speech and Stability Analysis. Developed a functional machine learning algorithm based voice and stability diagnostics. Presented findings at the Synopsys Science Fair. Gained experience in machine learning, data analysis, and health technology research.
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Summary
Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disease that often goes undiagnosed for months after symptom onset. To address this challenge, I developed a mobile app powered by machine learning that analyzes both speech patterns and physical stability to assist in the early detection of ALS. The app evaluates user voice recordings and balance data, comparing them to a dataset of healthy individuals and ALS patients to detect early signs of motor neuron degradation.
Through multiple model iterations, including Random Forest and Gradient Boosting algorithms, the system achieved strong diagnostic accuracy and demonstrated potential as an accessible, at-home screening tool for ALS. This research highlights how artificial intelligence and data science can be used for social good—improving healthcare accessibility and early intervention for patients in underserved areas.
Morteza
Polygence mentor
PhD Doctor of Philosophy
Subjects
Engineering, Biology, Computer Science
Expertise
Healthcare, Biotech and bioengineering, writing papers (any type), Engineering (especially Mechanical & Biomedical), Medical Device, Physics, Data Science, Programming, Code writing, Machine Learning, Image Processing, Mathematics, App Development
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Arnav
Student
Graduation Year
2026
Project review
“My project exceeded my expectations. I was able to explore my interests in machine learning and healthcare technology while developing an app that could make a real-world difference. The research process challenged me to think critically, manage data, and refine models independently.”
About my mentor
“My mentor, Morteza, was exceptional. He guided me through each stage of the research process, from model design to paper writing, and encouraged me to think deeply about the purpose behind my work. His mentorship helped me grow both technically and academically.”
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