Part of the course "ICT for Health" at Politecnico di Torino's Master's program "ICT for Smart Societies".
The first chapter contains a series of regression algorithms applied to a dataset to predict whether the patient has Parkinson's disease or not - using Unified Parkinson's Disease Rating Scale (UPDRS)
The second chapter contains the application of image analysis on the pictures of moles and predicting - by looking at the size of the mole - whether the patients have melanoma or not.
The third chapter deals with the prediction of the presence of kidney problems in patients - using Decision Tree Classifier.
The fourth chapter deals with the prediction of the presence of Arrythmia - and what type of arrythmia - in the patient using Bayes Classification, Neural Networks and Support Vector Machines.
The fifth chapter deals with the prediction of the possibility of a patient having Parkinson's Disease using their voices - using hidden markov models in Matlab.
This was my first exposure to machine learning. Except chapter 2 and 5, all the exercises had satisfactory results.