Application of Machine Learning Algorithms in Healthcare

Machine Learning (ML) is a subset of artificial intelligence that has made substantial strides in predicting and identifying health emergencies, disease populations, and disease state and immune response, amongst a few fields of healthcare. Here we provide a brief overview of machine learning-based approaches and learning algorithms. Second, we discuss a general procedure of ML and review some studies presented in ML application for several healthcare fields. We also briefly discuss the risks and challenges of ML application to healthcare. This dissertation also consists of four different cases in healthcare where we have applied ML techniques on real life data sets. In the first case study, Random Forest (RF) method has been used with high accuracy for classifying a rare skin disease Erythemato-squamous Dermatosis. In the second case study, Logistic regression analysis was utilized in finding the risk factors associated with Alcoholic hepatitis (AH). A sub-analysis was performed to determine variables associated with mortality in AH patients. In the third case study, Linear Discriminant Analysis (LDA) & RF were utilized for classifying five types of cancer (breast cancer, kidney cancer, colon cancer, lung cancer and prostate cancer) based on high dimensional microarray gene expression data. Principal component analysis (PCA) was used for dimensionality reduction, and principal component scores of the raw data for classification. In the fourth case study, we aim to discover the potential factors behind the initiation and then possibly sustain the desire to quit smoking using the LDA & RF method.