This retrospective-designed study was conducted after ethical approval was obtained (Eskisehir City Hospital, Non-Interventional Clinical Research Ethics Committee; Date: 16/02/2024; Decision Number: ESH/GOEK 2024/77). The medical records of the men whose semen had been analyzed for any reason between August 2021 and January 2023 at the Eskisehir City Hospital were collected. The exclusion criteria were: Aged < 18 or ˃50 years, diagnosis of azoospermia, low semen volume (less than 1.5 mL), abnormal genetics, history of any type of testicular or genitourinary tract or pelvic surgery, recurrent or subclinical varicocele, cryptorchidism, small-sized testis (normal testicular volume is 12.5–19 cc), treated cancer, vascular problem, hematologic illness, systemic disease, genitourinary system infection, or hormonal problems. After the application of the exclusion criteria, the men who also had data about their lifestyle behavior on file were included in the study. This data included details of their Body Mass Index (BMI), smoking and alcohol consumption, coffee intake, physical activity, sauna usage, cell phone usage, and the wearing of tight-fitting underwear as described previously [17, 18]. To ensure strict selection, ex-alcoholics and ex-smokers, passive smokers, and those who only participate in the other lifestyle factors irregularly were also excluded from the study. The lifestyle factors were coded ‘1’ if the BMI was ≥ 25, he smoked every day, drank any amount of alcohol, drank more than 3 cups of coffee a day, did not do any type of exercise regularly, regularly wore Tight underwear, went to a sauna-Turkish Bath regularly, or had a mobile phone ≥ 10 years during the 3-month window before semen collection. If a man who had a BMI of < 25, did not smoke, did not drink alcohol, did not drink more than 3 cups of coffee a day, exercised regularly, did not wear tight underwear, did not go to a sauna, or had used a cell phone < 10 years, the lifestyle factors were coded ‘0’. After collecting the data about the lifestyle behaviors, all semen analyses of the men included in the study were evaluated according to the WHO 2021 guideline. All semen analyses were categorized as normozoospermia (normal semen characteristic value) or abnormal. If oligozoospermia (sperm concentration < 16 × 106/ml of semen) and/or asthenozoospermia (motility < 30% spermatozoa with progressive motility), and/or teratozoospermia (morphologically normal spermatozoa < 4%) [1] had been detected in a semen sample, these results were categorized abnormal. All results were then grouped as normozoospermia, oligozoospermia (sperm concentration < 16 × 106/ml of semen), asthenozoospermia (motility < 30% spermatozoa with progressive motility), or teratozoospermia (morphologically normal spermatozoa < 4%). The 4 groups were analyzed separately by statistical methods and the ML algorithms were applied to each group.

The Shapiro–Wilk test was used to test the normality of data distribution. Continuous variables were expressed as mean ± standard deviation, median (minimum–maximum), and categorical variables were expressed as counts (percentages). Comparisons of normally distributed continuous variables between the materials/groups were performed using the student’s t-test. Comparisons of non-normally distributed continuous variables between the groups were performed using the Mann–Whitney U Test. Comparisons of categorical variables between the groups were performed using the Yates Chi-Square test and the Monte Carlo Chi-Square test. A two-sided P value < 0.05 was considered statistically significant.

The study was designed according to the principles of ML. The Extra Trees Classifier, Average (AVG) Blender, Light Gradient Boosting Machine (LGBM) Classifier, eXtreme Gradient Boosting (XGB) Classifier, Logistic Regression, and Random Forest Classifier techniques were used as ML algorithms. 70% of the data was used for training and the remaining 30% for testing. In the tests conducted with these models, the model success rates were determined based on accuracy, sensitivity, and specificity values with confusion matrix metrics and the area under curve (AUC) graph in the receiver operating characteristic (ROC) curve analysis. A confusion matrix, which contains information on actual and predicted classifications performed by a classification system and the performance of such systems, is generally assessed using the data in the matrix. Independent variables that significantly affect each group’s dependent variable were selected by the permutation feature importance method, which is based on a decrease in the model score when a single variable value is randomly shuffled (1).