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©The Author(s) 2024.
World J Psychiatry. Jun 19, 2024; 14(6): 804-811
Published online Jun 19, 2024. doi: 10.5498/wjp.v14.i6.804
Published online Jun 19, 2024. doi: 10.5498/wjp.v14.i6.804
Table 1 Comparison of age and whole volume between two groups
Variables | Healthy controls | Schizophrenia patients | t value | P value |
Age (yr) | 34.06 ± 8.88 | 35.04 ± 11.21 | -0.338 | 0.736 |
Whole volume (mm3) | 1203600.00 ± 88093.08 | 1164100.00 ± 112071.00 | 1.365 | 0.176 |
Table 2 The important features extracted by least absolute shrinkage and selection operator
Variables | Coefficients | |
Surface area | Bankssts | -0.00005308106 |
Inferior temporal | -0.00004908730 | |
Lateral occipital | -0.00003610912 | |
Lingual | -0.00022333510 | |
Insula | 0.000007207081 | |
Isthmus cingulate | 0.000156094500 | |
Paracentral | 0.000248385300 | |
Gray matter volume | Superior frontal | -0.00001763374 |
Temporal pole | 0.000149290400 | |
Cortical thickness | Lingual | 0.062200590000 |
Cuneus | 0.037246120000 | |
Lateral occipital | 0.239341600000 | |
Par sopercularis | 0.000000005200 |
Table 3 Comparison of morphological features between two groups
Variables | Schizophrenia patients | Healthy controls | t value | P value |
Left bankssts area | 1016.20 ± 170.13 | 1133.20 ± 186.00 | -2.488 | 0.015 |
Left inferior temporal area | 3492.30 ± 557.45 | 3856.30 ± 423.39 | -2.544 | 0.013 |
Left lateral occipital area | 4925.30 ± 719.19 | 5489.80 ± 480.09 | -3.111 | 0.003 |
Left lingual area | 2812.00 ± 444.30 | 3236.70 ± 479.05 | -3.471 | 0.001 |
Left lingual thickness | 2.08 ± 0.17 | 1.99 ± 0.10 | 2.121 | 0.037 |
Left superior frontal volume | 23123.00 ± 2824.16 | 24744.00 ± 2448.14 | -2.187 | 0.032 |
Right cuneus thickness | 2.01 ± 0.14 | 1.89 ± 0.14 | 2.938 | 0.004 |
Right lateral occipital thickness | 2.18 ± 0.15 | 2.07 ± 0.15 | 2.738 | 0.008 |
Table 4 Performance of each machine learning algorithm
Algorithms | AUC | Balanced accuracy, % | Sensitivity | Specificity |
GLM | 0.728 (0.470-0.986) | 61.84 | 0.737 | 0.500 |
RF | 0.886 (0.754-1.000) | 64.04 | 0.947 | 0.333 |
KNN | 0.601 (0.257-0.945) | 55.70 | 0.947 | 0.167 |
SVM | 0.842 (0.670-1.000) | 50.00 | 1.000 | 0.000 |
Table 5 The performance for each structural feature
Features | AUC | Balanced accuracy, % | Sensitivity | Specificity |
Surface area | 0.474 (0.241-0.706) | 39.50 | 0.789 | 0.000 |
Gray matter volume | 0.553 (0.235-0.871) | 56.60 | 0.632 | 0.500 |
Cortical thickness | 0.605 (0.327-0.884) | 55.70 | 0.947 | 0.167 |
- Citation: Yu T, Pei WZ, Xu CY, Deng CC, Zhang XL. Identification of male schizophrenia patients using brain morphology based on machine learning algorithms. World J Psychiatry 2024; 14(6): 804-811
- URL: https://www.wjgnet.com/2220-3206/full/v14/i6/804.htm
- DOI: https://dx.doi.org/10.5498/wjp.v14.i6.804