MUMBAI, India, Feb. 27 -- Intellectual Property India has published a patent application (202641018547 A) filed by Dr. S. V. Divya; Kumaresan M; Dr. P. Venkadesh; and Miruthula S J, Coimbatore, Tamil Nadu, on Feb. 18, for 'amyotrophic lateral sclerosis (als) early detection using hybrid machine learning.'
Inventor(s) include Dr. S. V. Divya; Kumaresan M; Dr. P. Venkadesh; and Miruthula S J.
The application for the patent was published on Feb. 27, under issue no. 09/2026.
According to the abstract released by the Intellectual Property India: "Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder that affects motor neurons, resulting in weak muscles, paralysis, and a reduced life span. For the development of better outcomes, it is important that early detection of ALS takes place, which results in early intervention. The Support Vector Machine (SVM) algorithm and Random Forest are popular classification techniques used for early detection of ALS, which relies on the machine learning model developed for this project. It aims to detect distinct patterns for the early occurrence of ALS. For the treatment of missing data, normalization of features, and removal of noise, pre-processing of the dataset is performed. Among the features, techniques for feature selection are employed to detect the features that are mainly essential for the detection of ALS. Just as the Random Forest model is employed to verify the correctness of predictions and prevent overfitting, the SVM classifier employs kernel learning to detect complex patterns. In verifying the feasibility of the models, cross-validation is employed. As Random Forest is more explanatory and SVM is more accurate in classification, the results demonstrate the capability of machine learning models to distinguish between early symptoms of ALS and healthy control data. Therefore, the system can provide automated."
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