Age Prediction Via Methylation Data and Machine Learning
Context: The goal of this project was to train a neural network to predict a person's age based on their blood sample (more specifically the methylation profile of their blood sample). In order to achieve this I downloaded a dataset of 752 raw methylation profiles. Each of these profiles contains over 400,000 features. Training the neural network on every one of these features would lead to overfitting. Therefore I calculated the absolute correlation of each of these features with respect to human age, and selected the 25 most correlated features. After training the neural network on a training set, the model achieved 100% accuracy plus or minus 10 years and over 90% accuracy plus or minus 5 years on the test set. See the poster below for more in depth information.