Preemptive Forecasting of Symptom Escalation in Cancer Patients Undergoing Chemotherapy.
Journal:
AMIA ... Annual Symposium proceedings. AMIA Symposium
Published Date:
Jan 1, 2024
Abstract
This study evaluates the utility of machine learning (ML) algorithms in early forecasting of total symptom score changes from daily self-reports of 339 chemotherapy patients. The dataset comprised 12 specific symptoms, with severity and distress for each symptom rated on a 1 to 10 scale, generating a "total symptom score" ranging from 0 to 230. To address the challenge of an unbalanced original dataset, where Class I (score change ≥ 5) and Class II (score change < 5) were unevenly represented, we created a balanced dataset specifically for model training. This process involved a stratified sampling technique to ensure equitable representation of both classes, enhancing the predictive analysis. Using the MATLAB® Classification Learner application, we investigated nine ML models, including decision trees, discriminant analysis, support vector machines (SVM), and others, each applying various classifiers. The objective was to predict the total symptom score change based on the preceding 3 to 5 days' symptom data. Models were trained on the balanced dataset to mitigate the original imbalance's impact, with comparative evaluations also conducted on the unbalanced data to assess performance differences. The analysis revealed that certain classifiers, such as SVM, delivered optimal performance on the unbalanced dataset, with an accuracy rate peaking at 82%. Yet, these models tended to frequently misclassify Class I as Class II. In contrast, the Ensemble algorithm equipped with the RUSBoost classifier demonstrated exceptional skill in accurately classifying both classes on both datasets, achieving accuracies of 59%, 59.3%, and 59.4% for data from 3, 4, and 5 days prior, respectively. Notably, these figures slightly improved to 61.16%, 58.41%, and 60.05% upon utilizing the balanced dataset for training. The deployment of a balanced dataset for model training underscores the significant potential of ML algorithms in improving symptom management for chemotherapy patients, offering a path to enhanced patient care and quality of life through targeted, personalized symptom monitoring.