Speech Processing for Early Alzheimer Disease Diagnosis: Machine Learning Based Approach

Abstract

Alzheimer's disease (AD) is a neurodegenerative disease characterized by the insidious onset of cognitive, emotional and language disorders. These attacks are sufficiently intense to affect the daily social and professional lives of patients. Today, in the absence of a reliable diagnosis and effective curative treatments, fighting this disease is becoming a real public health issue, prompting research to consider non-drug techniques. Among these techniques, speech processing is proving to be a relevant and innovative field of investigation. Several Machine Learning algorithms achieved promising results in distinguishing AD from healthy control subjects. Alternatively, many other factors such as feature extraction, the number of attributes for feature selection, used classifiers, may affect the prediction accuracy evaluation. To surmount these weaknesses, a model is suggested which include a feature extraction step followed by imperative attribute selection and classification is achieved using a machine learning classifiers. The current findings show that the proposed model can be strongly recommended for classifying Alzheimer's patient from healthy individuals with an accuracy of 79%.