Quantum-Enhanced AI Model Flags Sitting Time and Age as Top Predictors of Male Infertility Risk

Infertility affects roughly 15 percent of couples worldwide, and male factors account for an estimated 30 to 50 percent of those cases. Yet research into male infertility has long lagged behind the study of female reproductive health, in part because the condition is shaped by a tangled web of demographic, clinical, lifestyle, and environmental variables that traditional statistical methods struggle to untangle. A new study published in Frontiers in Medical Technology by T. Sarath and K. Brindha of Vellore Institute of Technology in India takes an ambitious swing at this problem, combining electronic health record data with a hybrid quantum-classical neural network designed to detect subtle patterns in small, noisy clinical datasets.

The research team built their framework around two public datasets. The first was a global fertility rate time series spanning 1960 to 2020, drawn from Kaggle, which captures decades of demographic change across countries. The second was an electronic health record-derived male fertility dataset containing 100 participants and 10 features, originally sourced from the UCI Machine Learning Repository. Each participant’s sperm concentration had been assessed against World Health Organization 2010 standards, and the features ranged from age and season of assessment to childhood diseases, surgical history, alcohol consumption frequency, smoking habits, and daily sitting duration. The diagnosis column, classifying each man as having normal or altered fertility, served as the prediction target.

Before any modeling began, the researchers put both datasets through a rigorous preprocessing pipeline. Missing years were removed from countries with incomplete temporal sequences in the fertility rate data, and values were normalized using Min-Max scaling before being converted into fixed-length sliding windows of ten years. For the male fertility dataset, categorical variables such as season and alcohol frequency were one-hot encoded, binary variables were label encoded, and numerical features like age and sitting hours were standardized. The team also checked for multicollinearity using Pearson’s correlation, retaining only features below a threshold of 0.85, and addressed class imbalance through random undersampling to preserve generalizability.

With the data prepared, the researchers deployed an unusually broad arsenal of Long Short-Term Memory (LSTM) architectures. LSTMs are a type of recurrent neural network designed to model sequential data while sidestepping the vanishing gradient problem through memory cells and gating mechanisms. The team tested unilateral LSTMs, which process sequences in a single forward direction; bidirectional LSTMs, which read data from both past and future contexts; hierarchical and attention-based LSTMs, which layer recurrent processing and dynamically weight the most informative time steps; convolutional LSTMs, which replace fully connected operations with convolutions inside the gating mechanism; LSTM autoencoders, which compress and reconstruct sequences; grid LSTMs, which extend recurrence across multiple dimensions; and cross-modal and associative LSTMs, which integrate information from multiple data types. Each variant was configured with 64 hidden units, tanh activation, sigmoid gates, a dropout rate of 0.2, the Adam optimizer, a learning rate of 0.001, and a batch size of 32, trained for 20 epochs with early stopping.

The centerpiece of the study, however, was a novel Quantum LSTM that embeds a Variational Quantum Circuit (VQC) directly within the LSTM’s input gating mechanism rather than using it as a postprocessing feature extractor, as most prior hybrid quantum-classical models have done. The quantum component used eight qubits with angle encoding via RX and RY rotations, two variational layers with CNOT entanglement gates, and a circuit depth of 18 gates per forward pass, yielding 64 trainable quantum parameters. The circuit replaced the input-to-hidden weight matrix in the LSTM’s input gate only, leaving the forget, output, and cell gates entirely classical. This architectural choice allows quantum superposition and entanglement to directly influence temporal memory updates, a distinction the authors argue sets their approach apart from earlier designs that treat quantum circuits as bolt-on feature enhancers.

To establish a fair baseline, the team also ran four conventional machine learning algorithms on both datasets: logistic regression, random forest, support vector machine, and XGBoost. XGBoost emerged as the strongest classical baseline, achieving 89.4 percent accuracy on the fertility rate data and 89.7 percent on the male fertility data, with random forest close behind at 88.3 and 89.2 percent respectively. These results confirmed that ensemble-based methods remain formidable competitors on small, structured datasets, but they also underscored the value of LSTM architectures for capturing sequential and temporal patterns that tree-based models cannot easily represent.

Among the classical LSTM variants, the unilateral LSTM proved the most consistent performer, reaching 91.2 percent accuracy on the fertility rate dataset in 79 seconds and 92.1 percent on the male fertility dataset in 82 seconds. The Quantum LSTM outperformed all classical models on both datasets, achieving 92.6 percent accuracy on the fertility rate data with a training time of 74 seconds and 93.8 percent on the male fertility data in 79 seconds. The absolute improvement over the best classical baseline was modest, roughly one to two percentage points, but the team validated it through three independent strategies: fivefold cross-validation to reduce bias, ten repeated random train-test splits to measure variance and stability, and 1,000 iterations of bootstrap resampling to quantify uncertainty through confidence intervals.

Statistical testing confirmed that the Quantum LSTM’s advantage was real rather than an artifact of sampling noise. The Friedman non-parametric test, applied across repeated validation runs, revealed significant differences among the LSTM architectures on both datasets, with P-values of 0.0009 for the fertility rate data and 0.0012 for the male fertility data. Pairwise Wilcoxon signed-rank tests comparing the Quantum LSTM against the unilateral LSTM across fivefold cross-validation yielded P-values of 0.031 and 0.024, both below the conventional 0.05 threshold, with small to moderate effect sizes (Cohen’s d of 0.42 and 0.48). The Quantum LSTM also exhibited narrower bootstrap confidence intervals and lower standard deviations than its classical counterpart, suggesting greater stability under repeated resampling even with limited data.

To make the model’s decisions transparent, the researchers applied SHAP (SHapley Additive exPlanations), an interpretability framework rooted in cooperative game theory that assigns each feature an importance value based on its marginal contribution to predictions. On the male fertility dataset, daily sitting duration emerged as the single most influential predictor, with a mean absolute SHAP value of approximately 0.22, followed closely by age at roughly 0.18. Season of assessment and frequency of alcohol consumption ranked third and fourth with importance scores of about 0.13 and 0.12 respectively. Clinical history variables, including smoking habits, childhood diseases, accidents or serious trauma, high fevers in the last year, and surgical intervention, contributed comparatively less, with SHAP values ranging from approximately 0.07 to 0.08. On the fertility rate dataset, SHAP analysis revealed that the most recent years, particularly 2019, carried the greatest predictive weight, while observations from earlier decades such as 1985 and 2001 contributed far less, indicating that the model relies on both short-term and long-term temporal dependencies.

The authors are careful to frame their findings as hypothesis-generating rather than clinically conclusive. The male fertility dataset contained only 100 participants from a single source, the quantum circuit was executed on a simulator rather than real quantum hardware, and all observed associations between fertility outcomes and lifestyle or clinical variables are correlational, not causal. The study also lacked genetic, hormonal, imaging, and longitudinal data, and the models are not intended for direct clinical deployment. Nevertheless, the work establishes a reproducible benchmarking framework that combines classical LSTM variants, a quantum-enhanced LSTM, and SHAP-based explainability for exploratory male infertility risk stratification. The researchers call for future validation on larger, multi-center electronic health record cohorts and on real quantum hardware, where gate noise, decoherence, and qubit connectivity constraints will test whether the modest gains observed in simulation translate into meaningful clinical advantage.

Subject of Research: A hybrid quantum-classical deep learning framework for interpretable male infertility risk prediction using electronic health record data

Article Title: A data-driven and interpretable hybrid quantum-classical model for male infertility risk assessment

Article References: Sarath, T., & Brindha, K. (2026). A data-driven and interpretable hybrid quantum-classical model for male infertility risk assessment. Frontiers in Medical Technology, 8, Article 1853728. https://doi.org/10.3389/fmedt.2026.1853728

Image Credits: AI Generated

DOI: 10.3389/fmedt.2026.1853728

Keywords: male infertility, quantum machine learning, LSTM, variational quantum circuit, electronic health records, SHAP, deep learning, fertility prediction, quantum computing, risk assessment, healthcare AI, reproductive medicine