Construction of Machine Learning Models to Predict Changes in Immune Function Using Clinical Monitoring Indices in HIV/AIDS Patients After 9.9-Years of Antiretroviral Therapy in Yunnan, China

OBJECTIVE: To investigate trends in clinical monitoring indices in HIV/AIDS patients receiving antiretroviral therapy (ART) at baseline and after treatment in Yunnan Province, China and to provide the basis for guiding clinical treatment to obtain superior clinical outcomes. METHODS: A total of 96 HIV/AIDS patients who had started and persisted in highly active ART treatment from September 2009 to September 2019 were selected. Of these, 54 had a CD4 cell count < 200 cells/μl while 42 had a CD4 cell count ≥ 200 cells/μl. Routine blood tests, liver and renal function, and lipid levels were measured before and 3, 6, 9, and 12 months after treatment. Lymphocyte subset counts and viral load were measured once per year, and recorded for analysis and evaluation. Three machine learning models (support vector machine [SVM], random forest [RF], and multi-layer perceptron [MLP]) were constructed that used the clinical indicators above as parameters. Baseline and follow-up results of routine blood and organ function tests were used to analyze and predict CD4(+) T cell data after treatment during long-term follow-up. Predictions of the three models were preliminarily evaluated. RESULTS: There were no statistical differences in gender, age, or HIV transmission route in either patient group. Married individuals were substantially more likely to have <200 CD4(+) cells/μl. There was a strong positive correlation between ALT and AST (r = 0.587) and a positive correlation between CD4 cell count and platelet count (r = 0.347). Platelet count was negatively correlated with ALT (r = -0.229), AST (r = -0.251), and positively correlated with WBCs (r = 0.280). Compared with the CD4 cell count < 200 cells/μl group, all three machine learning models exhibited a better predictive capability than for patients with a CD4 cell count ≥ 200 cells/μl. Of all indicators, the three models best predicted the CD4/CD8 ratio, with results that were highly consistent. In patients with a CD4 cell count < 200 cells/μl, the SVM model had the best performance for predicting the CD4/CD8 ratio, while the CD4/CD8 ratio was best predicted by the RF model in patients with a CD4 cell count ≥ 200 cells/μl. CONCLUSION: By the incorporation of clinical indicators in SVM, RF, and MLP machine learning models, the immune function and recuperation of HIV/AIDS patients can be predicted and evaluated, thereby better guiding clinical treatment.