--- title: Can machine learning predict which young women will have PMS? description: Yes. A computer model could identify women with PMS with fair accuracy. Key predictors were personality traits and life experiences, particularly neuroticism, url: https://niyora.com/research/papers/interpretable-machine-learning-models-for-identifying-premenstrual-syndrome-and-2025/ --- - [Niyora](https://niyora.com/) - [Research](https://niyora.com/research/) - [Library](https://niyora.com/research/library/) - Can machine learning predict which young women will have PMS? Randomised trial, 2025 # Can machine learning predict which young women will have PMS? Yes. A computer model could identify women with PMS with fair accuracy. Key predictors were personality traits and life experiences, particularly neuroticism, mindfulness level, history of painful periods, emotional abuse, and frequent painkiller use. Moderate study design 3447 young adult women ## What they did Researchers analyzed data from 3447 young adult women. All participants were classified as having PMS or not based on a screening score of 6 points on the Premenstrual Syndrome Scale. The data were divided into training and validation sets. Five different machine learning algorithms were tested to predict PMS. ## What they found The random forest algorithm performed best, with an accuracy score of 0.782. Among the women studied, 1474 had PMS and 1973 did not. Neuroticism was most strongly associated with PMS, followed by lower mindfulness, history of dysmenorrhea, history of emotional abuse, and use of analgesics. ## What it means for you This study shows that personality traits and past trauma are linked to PMS, not just physical factors. A machine learning model could help identify women at risk. However, the study is observational and collected data from only one point in time, so it cannot prove these factors cause PMS. 3447 young women analyzed 0.782 accuracy score of best model 1474 women with PMS ## About the study Original title Interpretable Machine Learning Models for Identifying Premenstrual Syndrome and Related Factors in Young Adult Women Authors Lin X, Zhao X, Zeng Y et al. Journal Research Square Year 2025 Study design Randomised trial Participants 3,447 What was tested None Open access Yes Read the paper [doi.org/10.21203/rs.3.rs-7522395/v1](https://doi.org/10.21203/rs.3.rs-7522395/v1) Original abstract Abstract Background Premenstrual syndrome (PMS) is a public health problem with widespread impact, influenced by the multiple factors. Young adult women, as a high-risk group for PMS, can benefit from early identification of key factors related to PMS. This study aimed to construct a machine learning model for identifying PMS among young adult women and explore the complex relationships between associated factors and PMS. Method A secondary analysis was performed using cross-sectional data from 3447 young adult women. Using a score of 6 points on the Premenstrual Syndrome Scale (PSS) as the cutoff, all participants were divided into the PMS group and the non-PMS group. The dataset was randomized into a training set and a validation set in 75% and 25% proportions. Five machine learning algorithms were used to develop models and the output of performed-best model was interpreted using SHapley Additiveex Planations (SHAP). Results There were 1474 women in PMS group and 1973 women in non-PMS group. Among five machine learning models, the random forest model performed best, with an AUC of 0.782. Neuroticism was most strongly associated with PMS, followed by mindfulness, history of dysmenorrhea, emotional abuse, and use of analgesic. Conclusion The results suggested that the random forest model was an effective tool for identifying PMS among young adult women, and neuroticism could serve as a crucial predictive indicator. Furthermore, interventions such as mindfulness training, dysmenorrhea management, and addressing early trauma may hold significant potential in preventing and managing PMS. Reproduced under CC-BY. ## Topics [Premenstrual syndrome (PMS)](https://niyora.com/research/topics/condition-pms/)[Dysmenorrhea and period pain](https://niyora.com/research/topics/condition-dysmenorrhea/)[Mindfulness and meditation](https://niyora.com/research/topics/treatment-mindfulness/) Research tells you what helps on average. Niyora helps on the day it hits. Calm down, see it another way, and decide what to say, with a PMS and PMDD app built by a doctor. ## Related studies Cohort study, 2018 Do women with PMS or period pain use more complementary therapies like vitamins or yoga over time? Cohort study, 2016 How many Australian women have PMS and do they use complementary medicine? Survey study, 2026 How common is PMS in female medical students and what helps? Review, 2025 Does yoga help with PMS and PMDD symptoms, and how might it work? Case report, 2023 Does acupuncture combined with supplements and meditation help PMS? Cohort study, 2026 Do sleep, exercise, and diet quality affect period pain, heavy bleeding, and PMS in midlife? Ask AI about this page Copy prompt ChatGPT ↗ Claude ↗ Perplexity ↗ Google AI Mode ↗ Grok ↗