Novel ensemble method for the prediction of response to fluvoxamine treatment of obsessive–compulsive disorder

Figures & data

Figure 1 Consort diagram of the study.

Abbreviation: Y-BOCS, Yale-Brown Obsessive–Compulsive Scale.

Table

Partition data into two groups: data with complete attributes and data with missing values

For each sample with missing values

X=Find k-nearest neighbors of complete samples that belong to the same class

For each features of sample with missing values

If data type of feature is nominal or ordinal, then find the mode of the values for that feature (in X samples) and impute to missing feature

else find the mean of the values of that feature and impute to missing feature

Table

Compute the weight of each feature based on MRMR algorithm and delete the redundant features

i=1, k=number_of_base classifiers (20 in our case)

Min=number_of_attributes/3;

Max=2*number_of_attributes/3;

While i≤k

t=generate a random number between Min and Max

New_attributes=select t attributes from all attributes using roulette wheel

Model=build a base classifier using New_attributes

If accuracy (model)>threshold (%75)

Base classifiers(i)= model

i=i+1

End if

End while

//test the model

For each of the test samples

If percent of base classifiers that agree on class label of test sample>threshold (%50)

Predict the class label of test sample using voting

Else

Predict the class label of test sample using best base classifier that obtained from previous section

End for

Abbreviation: MRMR, maximum relevance minimum redundancy.

Table 1 Clinical variables of patients in each treatment response classes

Variables	Responder	Nonresponder	Refractory	p value
Sex
Female	65	17	15	0.14
Male	30	17	7
Marital status
Single	30	12	8	0.86
Married	65	22	14
Occupation
Unemployed	61	14	13	0.06
Employed	34	20	9
Family history
Negative	14	5	5	0.63
Positive	81	29	17
Age at assessment
Mean	34.3	34.8	32.9	0.77
SD	10.6	9.9	8.5
Age of onset
Mean	24.0	20.9	20.6	0.18
SD	10.0	12.0	8.8
Illness duration
Mean	10.3	13.9	12.2	0.17
SD	9.8	8.7	11.9
Initial obsession score
Mean	10.3	12.4	12.0	0.04
SD	4.7	4.9	3.9
Initial compulsion score
Mean	9.3	9.4	8.6	0.85
SD	4.9	6.3	6.1
Initial total score
Mean	19.6	21.8	20.7	0.43
SD	8.2	10.0	8.7
Contamination obsession
Lack	29	4	10	0.01
Presence	66	30	12
Sexual obsession
Lack	72	16	19	0.001
Presence	23	18	3

Abbreviation: SD, standard deviation.

Table 2 Accuracy, sensitivity, and specificity of different classification algorithms applied on the current OCD data set based on 20 repetitions of 10-fold cross-validation

Algorithms	Accuracy (%) (CI)	Recall (sensitivity) (%) (CI)	Specificity (%) (CI)
MLP	64 (56–71.2)	46 (38.2–53.9)	73 (65.4–79.4)
Decision tree	76 (68.6–82.1)	64 (56–71.2)	82 (75.1–87.3)
KNN	74 (66.4–80.3)	61 (53–68.4)	80 (72.9–85.6)
SVM	74 (66.4–80.3)	62 (54–69.3)	80 (72.9–85.6)
Random forest	75 (67.5–81.2)	62 (54–69.3)	81 (74–86.4)
New method	86 (79.5–90.6)	79 (71.8–84.7)	89 (83–93)

Abbreviations: CI, confidence interval; KNN, k-nearest neighbor; MLP, multilayer perceptron; OCD, obsessive–compulsive disorder; SVM, support vector machine.

Table 3 TP, FP, FN, and TN values for each treatment response class resulted from some algorithms and new method

Algorithms	TP	FP	FN	TN
SVM
Responders	85	44	10	12
Nonresponders	7	8	27	109
Refractory	2	5	20	124
Random forest
Responders	89	47	6	9
Nonresponders	5	8	29	109
Refractory	1	1	21	128
Decision tree
Responders	72	24	23	32
Nonresponders	18	20	16	97
Refractory	7	10	15	119
Proposed method
Responders	83	13	12	43
Nonresponders	23	11	11	106
Refractory	13	8	9	121

Abbreviations: FN, false negative; FP, false positive; TN, true negative; TP, true positive; SVM, support vector machine.

Table 4 Accuracy, sensitivity, and specificity of each of class resulted from the new method

Patients’ class	Accuracy (%)	Recall (sensitivity) (%)	Specificity (%)
Class 1 (responder)	83	87	77
Class 2 (nonresponder)	85	68	90
Class 3 (refractory)	89	59	94