Predicting Patient Sexual Function After Prostate Surgery Using Machine Learning

A major health concern for men is prostate cancer. An accurate prediction of patients' conditions after surgery is essential for understanding their quality of life. For improving patient care, medical and healthcare professionals use machine learning for a variety of purposes. By using supervised machine learning algorithms, we aim to identify the most reliable predictors of patient sexual function one year after surgery. We used the EPIC-26 (Expanded Prostate Index Composite-26) questionnaire to assess the patient's quality of life and sexual function. An approximate 500 patient sample was used in our case study to test the effectiveness of this methodology. Based on demographic and clinical data collected prior to surgery, our model predicts patient self-assessment of sexual function with high accuracy one year after surgery. In order to improve and enhance the quality of the patient experience, the methodology presented can support clinical decisions.