Analyzing Medical Data by Using Statistical Learning Models

In this work, we investigated the prognosis of three medical data specifically, breast cancer, heart disease, and prostate cancer by using 10 machine learning models. We applied all 10 models to each dataset to identify patterns in them. Furthermore, we use the models to diagnose risk factors that increases the chance of these diseases. All the statistical learning techniques discussed were grouped into linear and nonlinear models based on their similarities and learning styles. The models performances were significantly improved by selecting models while taking into account the bias-variance tradeoffs and using cross-validation for selecting the tuning parameter. Our results suggests that no particular class of models or learning style dominated the prognosis and diagnosis for all three medical datasets. However nonlinear models gave the best predictive performance for breast cancer data. Linear models on the other hand gave the best predictive performance for heart disease data and a combination of linear and nonlinear models for the prostate cancer dataset.