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Machine Learning Models for Predicting Pain, Fatigue, Depression, Anxiety, and Malnutrition in Cancer Patients: A
Maidenamu Reheman1, Yunhuan Li1,2, Yang Chen1
1Department of Nursing, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, Sichuan, People's Republic of China.
Introduction:
Cancer-related symptoms including pain, fatigue, depression, anxiety, and malnutrition drive poor quality of life and adverse clinical outcomes in cancer patients. While machine learning (ML) models are increasingly developed to predict these symptoms, existing studies are marked by significant heterogeneity in algorithms, sample sizes, and predictors, and lack quantitative synthesis of model performance, methodological quality, and clinical applicability. This study aimed to comprehensively summarize the characteristics of models and predictors, evaluate the predictive accuracy, risk of bias, and clinical applicability of ML prediction models.
Design:
Systematic review and meta-analysis.
Methods:
A comprehensive literature search was conducted in PubMed, Web of Science, the Cochrane Library, CINAHL, PsycINFO, CNKI, WanFang, VIP, and SinoMed, from database inception to August 31, 2025. Data were extracted in accordance with the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS), and the risk of bias and applicability of included models were assessed using the Prediction Model Risk of Bias Assessment Tool and Artificial Intelligence (PROBAST-AI). The quality of evidence was evaluated using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework. A random-effects model was employed for pooled analysis. Subgroup analyses were stratified by cancer type, geographic region, and algorithm type.
Results:
A total of 11,217 records were retrieved, and 34 studies were included in the analysis. The pooled AUCs for predicting pain, fatigue, depression, anxiety, and malnutrition were 0.76 (95% CI: 0.69-0.83, I2 = 97.7%), 0.82 (95% CI: 0.76-0.88, I2 = 98.5%), 0.76 (95% CI: 0.70-0.82, I2 = 98.4%), 0.78 (95% CI: 0.69-0.86, I2 = 37.9%), and 0.86 (95% CI: 0.80-0.91, I2 = 94.9%), respectively. Subgroup analyses across cancer type, geographical region, and algorithm type revealed no statistically significant sources of heterogeneity. The certainty of evidence was moderate across all outcomes.
Conclusion:
This systematic review and meta-analysis showed that ML models achieved acceptable discriminative performance for predicting pain, anxiety, depression, fatigue, and malnutrition in patients with cancer in available datasets. Given predominant internal validation and observed heterogeneity, clinical utility requires further prospective validation and implementation studies. Future research may consider theory-driven predictors and clinically tailored algorithms to improve model performance.
Clinical Relevance:
These pooled findings provide a preliminary foundation for the clinical translation of ML models to predict pain, anxiety, depression, fatigue, and malnutrition in cancer patients. Further prospective validation in diverse clinical settings and randomized controlled trials evaluating the effectiveness of model-guided symptom management strategies are needed to improve patient outcomes.
Prospero Registration:
CRD420251130183.
