Alzheimer’s and Parkinson’s Prediction

Progressive neurological diseases are untreatable and thus the only way of preventing these disease is to detect them early. This early detection is difficult too since the signs of these disease can show up very late in any lab reports, MRIs and/or behaviour of the patient. The presence of large scale studies and databases such as the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Parkinson’s Progression Markers Initiative (PPMI) has given hope to the efforts of machine learning researchers to contribute towards an early detection system. Our lab works with the different modalities of data in these studies with the aim of developing machine learning algorithms that can aid in both the classification and detection of patients with/or developing these diseases.

Publications:

• Devendra Singh Dhami, Ameet Soni, David Page, Sriraam Natarajan, “Identifying Parkinson’s Patients : A Functional Gradient Boosting Approach”, Artificial Intelligence in Medicine (AIME) (2017).
• Sriraam Natarajan, Baidya Saha, Saket Joshi, Adam Edwards, Tushar Khot, Elizabeth M. Davenport, Kristian Kersting, Christopher T. Whitlow and Joseph A. Maldjian. “Relational Learning helps in Three-way Classification of Alzheimer Patients from Structural Magnetic Resonance Images of the Brain” International Journal of Machine Learning and Cybernetics, Springer 2013.
• Sriraam Natarajan, Saket Joshi, Baidya N. Saha, Adam Edwards, Elizabeth Moody, Tushar Khot, Kristian Kersting, Christopher T. Whitlow and Joseph A. Maldjian. “A Machine Learning Pipeline for Three-way Classification of Alzheimer Patients from Structural Magnetic Resonance Images of the Brain” IEEE Conference on Machine Learning and Applications (ICMLA), 2012.