Computational and Mathematical Methods in Medicine / 2018 / Article / Tab 4 / Research Article
Linearized and Kernelized Sparse Multitask Learning for Predicting Cognitive Outcomes in Alzheimer’s Disease Table 4 Performance comparison of various methods with fusing multiple modalities data in terms of CC and wR on 10 cross validation cognitive prediction tasks.
Method ADAS MMSE FLU TRAILS ANIM A B 0.670 ± 0.091 0.539 ± 0.117 0.481 ± 0.112 0.417 ± 0.115 0.525 ± 0.073 0.619 ± 0.058 0.482 ± 0.087 0.395 ± 0.105 0.385 ± 0.120 0.501 0.700 ± 0.070 0.549 ± 0.108 0.486 ± 0.119 0.437 ± 0.119 0.567 ± 0.070 0.705 ± 0.067 0.560 ± 0.096 0.527 ± 0.102 0.450 ± 0.115 0.575 ± 0.064 0.677 ± 0.093 0.512 ± 0.113 0.464 ± 0.095 0.411 ± 0.113 0.529 ± 0.094 0.634 ± 0.056 0.493 ± 0.100 0.410 ± 0.133 0.375 ± 0.090 0.478 ± 0.061 0.710 ± 0.060 0.537 ± 0.106 0.472 ± 0.111 0.426 ± 0.105 0.566 ± 0.081 0.727 ± 0.062 0.551 ± 0.112 0.512 ± 0.097 0.444 ± 0.099 0.582 ± 0.065 0.673 ± 0.096 0.548 ± 0.124 0.491 ± 0.095 0.422 ± 0.135 0.528 ± 0.102 0.631 ± 0.057 0.488 ± 0.108 0.418 ± 0.119 0.386 ± 0.095 0.524 ± 0.065 0.714 ± 0.067 0.566 ± 0.107 0.499 ± 0.094 0.437 ± 0.122 0.583 ± 0.077 0.721 ± 0.064 0.574 ± 0.105 0.512 ± 0.094 0.445 ± 0.120 0.589 ± 0.073 Method RAVLT wR TOTAL TOT6 T30 RECOG 0.576 ± 0.077 0.536 ± 0.085 0.516 ± 0.041 0.444 ± 0.079 0.523 ± 0.082 0.548 ± 0.103 0.497 ± 0.124 0.490 ± 0.092 0.409 ± 0.098 0.481 ± 0.081 0.593 ± 0.079 0.547 ± 0.086 0.529 ± 0.038 0.450 ± 0.075 0.540 ± 0.077 0.618 ± 0.072 0.563 0.546 ± 0.027 0.446 ± 0.085 0.554 ± 0.069 0.585 ± 0.069 0.533 ± 0.093 0.511 ± 0.044 0.434 ± 0.077 0.517 ± 0.079 0.559 ± 0.110 0.508 ± 0.111 0.503 0.432 ± 0.081 0.488 ± 0.075 0.617 0.561 ± 0.100 0.541 ± 0.057 0.462 ± 0.079 0.543 ± 0.075 0.654 0.577 ± 0.082 0.560 ± 0.038 0.444 ± 0.087 0.561 ± 0.068 0.594 0.554 ± 0.080 0.536 ± 0.033 0.459 ± 0.071 0.534 ± 0.082 0.563 0.510 ± 0.111 0.501 ± 0.081 0.436 ± 0.095 0.495 ± 0.072 0.621 0.582 ± 0.083 0.564 ± 0.046 0.475 ± 0.073 0.560 ± 0.071 0.637 0.593 ± 0.077 0.575 ± 0.041 0.479 ± 0.081 0.570 ± 0.067
