Non-Invasive Machine Learning Classification Models for Early Alzheimer’s disease Detection

Machine Learning is mainly concerned with the development of algorithms that allow a computer to learn from the data and past experiences on their own. It has a great scope in healthcare, as healthcare is becoming more personalized. Here we will apply machine learning in Alzheimer's. Alzheimer’s disease also known by the term AD, is a prevailing neurodegenerative disorder that attacks the cognitive skills as well as the memory of human beings. While there is no known treatment for cure, the earlier detection becomes the need of time. Also, earlier detection of its symptoms may result in a less expensive and timelier diagnosis. As the disease makes it difficult for the patient to do even the daily activities, taking actions to fight against Alzheimer's will benefit in increasing the independence of patients and quality of life if detected earlier. In this paper, a review of some simple non-invasive machine learning classification techniques is done where the prediction of disease is done based on verbal and non-verbal communication data of subjects. This data is very easy to get from the subject and so these techniques are harmless, very economical, and can help in diagnosis much earlier as compared to other techniques. After review, it is concluded that these techniques are very much efficient. Future research on these techniques may help in increased accuracy of diagnosis which may help the subjects to catch the symptoms very much earlier and save their conditions from deteriorating further.