['To test the following hypotheses: (1) eyes from individuals with human immunodeficiency virus (HIV) have electrophysiologic abnormalities that manifest as multifocal electroretinogram (mfERG) abnormalities; (2) the retinal effects of HIV in immune-competent HIV individuals differ from the effects in immune-incompetent HIV individuals; (3) strong machine learning classifiers (MLCs), like support vector machine (SVM), can learn to use mfERG abnormalities in the second-order kernel (SOK) to distinguish HIV from normal eyes; and (4) the mfERG abnormalities fall into patterns that can be discerned by MLCs. We applied a supervised MLC, SVM, to determine if mfERGs in eyes from patients with HIV differ from mfERGs in HIV-negative controls.', 'Ninety-nine HIV-positive patients without visible retinopathy were divided into 2 groups: (1) 59 high-CD4 individuals (H, 104 eyes), 48.5 +/- 7.7 years, whose CD4 counts were never observed below 100, and (2) 40 low-CD4 individuals (L, 61 eyes), 46.2 +/- 5.6 years, whose CD4 counts were below 100 for at least 6 months. The normal group (N, 82 eyes) had 41 age-matched HIV-negative individuals, 46.8 +/- 6.2 years. The amplitude and latency of the first positive curve (P1, hereafter referred to as a) and the first negative curve (N1, referred to as b) in the SOK of 103 hexagon patterns of the central 28 degrees of the retina were recorded from the eyes in each group. SVM was trained and tested with cross-validation to distinguish H from N and L from N. SOK was chosen as a presumed detector of inner retinal abnormalities. Classifier performance was measured with the area under the receiver operating characteristic (AUROC) curve to permit comparison of MLCs. Improvement in performance and identification of subsets of the most important features were sought with feature selection by backward elimination.', 'In general, the SOK b-parameters separated L from N and H from N better than a-parameters, and latency separated L from N and H from N better than amplitude. In the HIV groups, on average, amplitude was diminished and latency was extended. The parameter that most consistently separated L from N and H from N was b-latency. With b-latency, SVM learned to distinguish L from N (AUROC = 0.7.30 +/- 0.044, P = .001 against chance [0.500 +/- 0.051]) and H from N (0.732 +/- 0.038, P = .0001 against chance) equally well. With best-performing subsets (21 out of 103 hexagons) derived by backward elimination, SVM distinguished L from N (0.869 +/- 0.030, P < .00005 against chance) and H from N (0.859 +/- 0.029, P <.00005 against chance) better than SVM with the full set of hexagons. Mapping the top 10 hexagon locations for L vs N and H vs N produced no apparent pattern.'] Does analysis with support vector machine show HIV-positive subjects without infectious retinitis have mfERG deficiencies compared to normal eyes?