Machine Learning Methods for Optimal Prediction of Outcome in Parkinson’s Disease

In the present work, we systematically probe a range of predictor machines (11 machines), and aim to find the best combinations of features to result in improvements in prediction of outcome in PD. First, we created 32 combinations of 18 conventional features experimentally and selected 4 arrangements of 204 PD subjects. The combinations were applied to the various predictor machines), thereby absolute error of the best combination reached 4.3 (in prediction of MDS-UPDRS-III motor performance in year 4). This is in comparison to previous works that attained errors of around 9. In second part, subset selector machines were used for selecting the best combinations between all features, and GA and ACO selector machines selected the best combinations, further lowering error when combined with LOLIMOT for prediction. Selected features by GA and ACO (UPDRS I-Year 1, UPDRS III-Year 1, left putamen Uptake-Year 1, Age, Gender) had positive effect on prediction of outcome and mean absolute error reached 4.15. Moreover, other subset selector machines also reached acceptable results; mean absolute errors in some predictor machines were below 4.7. Overall, LOLIMOT was seen as the best predictor machine, and GA and ACO as the best feature subset selector machines. Furthermore, MDS-UPDRS III-years 0 and 1, MDS-UPDRS II- year 1, MDS-UPDRS I- years 0 and 1, age and DAT SPECT putamen as well as caudate uptake -year 1 were seen as most important predictors of outcome.