  In this paper, an interpretable classifier using an interval type-2 fuzzy
neural network for detecting patients suffering from Parkinson's Disease (PD)
based on analyzing the gait cycle is presented. The proposed method utilizes
clinical features extracted from the vertical Ground Reaction Force (vGRF),
measured by 16 wearable sensors placed in the soles of subjects' shoes and
learns interpretable fuzzy rules. Therefore, experts can verify the decision
made by the proposed method based on investigating the firing strength of
interpretable fuzzy rules. Moreover, experts can utilize the extracted fuzzy
rules for patient diagnosing or adjust them based on their knowledge. To
improve the robustness of the proposed method against uncertainty and noisy
sensor measurements, Interval Type-2 Fuzzy Logic is applied. To learn fuzzy
rules, two paradigms are proposed: 1- A batch learning approach based on
clustering available samples is applied to extract initial fuzzy rules, 2- A
complementary online learning is proposed to improve the rule base encountering
new labeled samples. The performance of the method is evaluated for classifying
patients and healthy subjects in different conditions including the presence of
noise or observing new instances. Moreover, the performance of the model is
compared to some previous supervised and unsupervised machine learning
approaches. The final Accuracy, Precision, Recall, and F1 Score of the proposed
method are 88.74%, 89.41%, 95.10%, and 92.16%. Finally, the extracted fuzzy
sets for each feature are reported.
