Applying Predictive Analytics: The Role of Artificial Neural Networks in Predicting Alzheimer’s Disease

Applying Predictive Analytics: The Role of Artificial Neural Networks in Predicting Alzheimer’s Disease

Applying Predictive Analytics: The Role of Artificial Neural Networks in Predicting Alzheimer’s Disease

This monthly blog highlights and discuss emerging trends and challenges related to healthcare data and its ever changing life cycle.

The phrase “Longevity Revolution,” referring to the increase in human life expectancy by 30 years, isn’t new anymore. Various books have been written on this subject, and it is a widely discussed topic in conferences these days. It is a no-brainer that this increase in life expectancy warrants preparedness to be able to support oneself financially, psychologically, sociologically, and physiologically for those additional years. Every preparation aside, however, there is one obstacle that has yet to be conquered: we haven’t yet been able to identify how best to prepare for or guard against Alzheimer’s disease. Alzheimer’s is a progressive disease that impacts the brain and its cognitive abilities. Its symptoms gradually worsen with time. It has no cure (yet), is fatal, and adversely impacts the quality of life of all involved—patients, family members, and care givers.

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While efforts are underway to identify the causes and find a cure for Alzheimer’s disease, this post intends to explore whether Artificial Neural networks (ANNs) can be used to predict the onset of Alzheimer’s disease. ANN is a computational paradigm that has a structure and operation that resembles that of a mammal brain. The hypothesis is that just as a mammal brain can do intelligent and abstract things like image recognition and signal processing with speed, accuracy, and ease, building a computational paradigm on the principles of mammal brain will result in computer-driven intelligent decision making. Mammal brains function through inter-connected neurons whose activation leads to a decision making pathway, and hence so does ANN. The following table illustrates the underlying analogy:

ANNs have been established to be useful algorithms in situations where we can collect lots of data but are unable to identify the features that are important to predict a certain outcome. ANNs work in these situations because of their ability to start with given weight values (and hence the feature set—weighted combination of input data elements) and improvise them using inputs from multiple inter-connected nodes, and the value of cost function. Though a key limitation to be kept in mind is features/predictors hence found through ANNs are often difficult to explain in simple terms, unlike in techniques like Regression where features are relatively easy to decipher.

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Causes of Alzheimer’s (late-onset) aren’t yet fully known.

 



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