Background: Accurate prognostic prediction is crucial for managing Idiopathic Sudden Sensorineural Hearing Loss (ISSHL). Previous studies developing ISSHL prognosis models often overlooked individual variability in hearing damage by relying on fixed frequency domains. This study aims to develop models predicting ISSHL prognosis one month after treatment, focusing on patient-specific hearing impairments. Methods: Patient-Personalized Seigel’s Criteria (PPSC) were developed considering patient-specific hearing impairment related to ISSHL criteria. We performed a statistical test to assess the shift in the recovery assessment when applying PPSC. The utilized dataset of 581 patients comprised demographic information, health records, laboratory testing, onset and treatment, and hearing levels. To reduce the model’s reliance on hearing level features, we used only the averages of hearing levels of the impaired frequencies. Then, model development, evaluation, and interpretation proceeded. Results: The chi-square test (p-value: 0.106) indicated that the shift in recovery assessment is not statistically significant. The soft-voting ensemble model was most effective, achieving an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.864 (95% CI: 0.801–0.927), with model interpretation based on the SHapley Additive exPlanations value. Conclusions: With PPSC, providing a hearing assessment comparable to traditional Seigel’s criteria, the developed models successfully predicted ISSHL recovery one month post-treatment by considering patient-specific impairments.

Although neuromuscular junction disorders (NMDs) and inflammatory polyneuropathies (IPNs) are biologically distinct, direct genetic comparisons between them remain limited, suggesting that additional underlying biological differences may yet be uncovered. Few studies have explored whether differences in variant patterns within shared biological pathways can be leveraged to distinguish NMDs and IPNs using machine learning (ML). We propose an interpretable ML framework based on Pathway-based Genetic Variant Dosage Average (PGVDA) to classify NMDs and IPNs and to identify key genes and pathways differentiating diseases. Using nonsynonymous variants from 667 UK Biobank participants, logistic regression identified disease-associated variants. Significant pathways were identified via pathway enrichment analysis with adjusted P-value < 0.05. PGVDA was calculated by assigning the log odds ratio to each variant dosage and then computing a weighted average at the pathway level. Dimensionality reduction was performed via hierarchical clustering based on gene-set overlaps and then PGVDAs with a variance inflation factor (VIF) > 10 were excluded. ML models were evaluated using leave-one-out cross validation. Utilizing the best-performing model, SHAP-based interpretation was applied using two distinct input configurations. Pathway-level interpretation using PGVDA input included stages of PGVDA scaling and ML-based classification, while variant-level interpretation using variant dosage input encompassed stages from odds ratio-based weight assignment to ML-based classification. Using logistic regression model with best performance, key differentiating five PGVDAs and 10 genes within each pathway were identified, suggesting that pathway-level variant aggregation enables accurate and interpretable classification of these two neuromuscular diseases. External validation is needed to ensure generalizability across populations.