Machine Learning Applications to the Diagnosis of Neurodegenerative Diseases

Abstract

Imagine you are enjoying a game of Pictionary with your family. As the picturist, you pick up a card from the deck. The card reads “umbrella” as you flip it over. You quickly start sketching an umbrella as the sand timer begins its one minute countdown. As you draw, a family member analyzes the drawing to guess the word. This game of Pictionary is analogous to machine learning, which is a type of artificial intelligence. Artificial intelligence (AI) is broadly defined as the use of computer algorithms in a way that imitates critical analysis and thinking analogous to humans. Machine learning is a subset of AI that allows computer algorithms to make accurate predictions based on a set of data. As children, we are shown pictures of objects, including umbrellas, and are taught that the image of an umbrella correlates to the word umbrella. This is the process of learning. Having seen umbrellas multiple times, our brains learn to associate the image with the word and can now recognize umbrellas. Similar to how our brains learn, machine learning allows for a set of computer algorithms (also known as a model) to learn by being shown a set of data and taught the patterns among it. The model can then make predictions based on a new set of data by applying the patterns it learned. As artificial intelligence (AI) improves efficiency and accuracy, it is emerging as a powerful tool to aid in providing solutions in multiple complex fields. Medicine is an example of a field that AI is used for, particularly the areas of diagnosis and treatment. Since neurodegenerative diseases at present have no cures, early diagnosis and avoiding misdiagnosis are crucial to ensuring patients have a good quality of life [3]. This article will investigate the application of machine learning techniques to the diagnosis and treatment planning of neurodegenerative diseases.