A non-invasive method for prediction of neurodegenerative diseases using gait signal features.

The steady degeneration of neurons is the hallmark of neurodegenerative illnesses, which are, by definition, incurable. Corticobasal Syndrome (CS), Huntington's Disease (HD), Dementia, Amyotrophic Lateral Sclerosis (ALS), Progressive supranuclear palsy (PSP) and Parkinson's Disease (PD) are some of the common neurodegenerative diseases which has impacted millions of people, predominantly among the older population. Various computational techniques, including but not limited to machine learning, are emerging as discrimination and detection of neuro-related diseases. This research proposed a machine learning-based framework to correctly detect PD, HD, and ALS from the gait signals of subjects both in binary and multi-class detection environment. The detection approach proposed here combines the classification power of Naïve Bayes and Logistic Regression jointly in a modern UltraBoost ensemble framework. The proposed method is unique in its ability to detect neuro diseases with a small number of gait features. The proposed approach ascertains most essential gait features through three state-of-the-art feature selection schemes, infinite feature selection, infinite latent feature selection and Sigmis feature selection. It has been observed that the gait signal features of the subjects are identified through Infinite Feature Selection manifests better detection results than the features obtained through Infinite Latent Feature and Sigmis feature selection while detecting Parkinson's and Huntington's Disease in a multi-class environment. So far as the binary detection environment is concern, the Amyotrophic lateral sclerosis is detected with 99.1% detection accuracy using 18 Sigmis gait features, with 99.1% sensitivity and 98.9% specificity, respectively. Similarly, Huntington's disease was detected with 94.2% detection accuracy, 94.2% sensitivity, and 94.5% specificity using 5 Sigmis gait features. Finally, Parkinson's disease was detected with 98.4% sensitivity, specificity, and detection accuracy.