Predicting the probability of complications during prostatectomy in pa-tients with prostate cancer using machine learning methods

Objective. To determine the probabilities of predicting possible complications after surgery in patients with the diagnosis of prostate cancer using artificial intelligence methods.

Materials and methods. Case histories of 701 patients who underwent prostatectomy were analyzed in the study. The anamnesis, findings of clinical, laboratory and instrumental study, as well as objective data of clinical observations were evaluated. The average age was 64.72. On the basis of the set of examination results, patients were selected according to the following inclusion criteria: prostate cancer patients without confirmed metastases with disease stage from T1N0M0 to T3N0M0; absence of previous and concomitant special treatment (immunotherapy or targeted therapy); informed consent to the surgery. Logistic regression, a binary classifier using a sigmoidal activation function on linear combinations of features, was used as a machine learning model.

Results. It was determined that the logistic regression model based on selected parameters (prostate volume, pain syndrome, disease duration), predicts the probability of complications quite well (TPR = 1). The overall accuracy of the model is: Accuracy = 0.98. At the same time, it can be noticed from the agreement matrix that the trained model plays it safe and classifies some cases without complications incorrectly in 5.3 % (FNR = 0.053). However, the model never made an error and did not classify cases with a high risk of complications as those in which such a possibility was unlikely.

Conclusions. The results obtained show that on the basis of just three parameters (prostate volume, pain syndrome, duration of the disease), it is possible to build a fairly good predictive model of the probability of complications after prostatectomy based on such machine learning method as logistic regression.