Multi-Instance Learning for Parkinson's Tremor Level Detection with Learnable Discriminative Pool

Parkinson's disease (PD) is a neurodegenerative disorder characterized by tremors as its most typical symptom. Wearable accelerometer sensors, along with corresponding machine learning algorithms, can effectively assist in the diagnosis of PD tremors. However, due to the variations in disease progression and symptoms caused by individual differences among PD patients, it is challenging for existing algorithms to eliminate label noise and accurately identify and extract disease-related features across diverse patient data. In this study, we propose a Learnable Discriminative Instance Pool (LDIP) algorithm based on multi-instance learning, which integrates the concept of learnable shapelets. This method transforms the traditional DIP algorithm into a learnable instance pool that can be adaptively adjusted according to discriminative criteria, thereby enhancing the separability between different classes after bag mapping. We evaluated the proposed method on two clinical datasets using three different machine learning classifiers, achieving a maximum 73% accuracy for 5-class classification. The experimental results demonstrate that our proposed method consistently outperforms current baselines across various settings.