Symptom-Based Classification of Migraine Severity Using Random Forest and LassoNet - Syehan Fariz Gustomo; Kemas Muslim Lhaksmana (Forum Kerjasama Pendidikan Tinggi, 2026)
This study aims to develop and compare classification models for predicting symptom-based migraine intensity using a public dataset from Kaggle. The research was conducted on multiclass data with an imbalanced class distribution. Therefore, target creation and result interpretation must be carefully designed so that the resulting evaluation remains consistent with the characteristics of the data used. In this study, the Intensity variable was recoded into three operational classes: low, moderate, and high. The features used included symptoms, characteristics of migraine episodes, and `symptom_count`, which represents the number of symptoms in each sample. The two models compared were Random Forest and LassoNet, both of which were tested using Stratified 5-Fold Cross-Validation. Model performance was assessed using the Macro F1-score as the primary metric, Balanced Accuracy as the main supplementary metric, and Accuracy as a complementary metric. The test results showed that Random Forest performed better, with a Macro F1-score of 0.6366, Balanced Accuracy of 0.6181, and Accuracy of 0.6225. Meanwhile, LassoNet achieved a Macro F1-score of 0.2474, a Balanced Accuracy of 0.3333, and an Accuracy of 0.5900. These results indicate that symptom patterns in the dataset can still be utilized to distinguish migraine intensity within a computational classification framework, although the separation between closely related classes is not yet fully robust.