Data Science Techniques to Support Prediction, Diagnosis and Recode Treatment of Alzheimer'S Disease

Data science is the process of liberating meaning from raw data using scientific methods and algorithms, and is becoming much more commonly used in healthcare with the emergence of personalised healthcare. Alzheimer's disease (AD) is a neurodegenerative disease that has no proven curative treatment, however a new treatment protocol, ReCODE, has been proposed to slow and reverse the progression of the disease. In this paper, an overview of AD is provided, followed by a description of the ReCODE protocol, including the new proposed methods and data to be used in prediction diagnosis and treatment. The ways in which data science can help with prediction and diagnosis are then reviewed, along with the data science techniques that can help with each treatment in the protocol. It is concluded that current data science techniques are useful in aiding the successful treatment of AD patients with he ReCODE protocol, and though there is much promise to the use of data science techniques to predict and diagnose AD, no such technique yet exists that can process all the necessary data. Future research should be conducted to develop such a data science technique. Further research should also be conducted to improve current data science techniques used to support the treatment of AD.