A Novel Direction for Non-Invasive Diabetes Prediction: Integrating Unani Wisdom with Sensor Technology
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Abstract
This study aims to modernize disease diagnosis in Unani medicine by utilizing signal processing techniques. Various features such as systolic and diastolic peaks, peak-to-peak interval, augmentation, and stiffness index were extracted from the pulse signals. These features were then used to train predictive models, including KNN, J48, and Random Forest. Predictive models achieved high precision rates: 100% for KNN, 99.2% for J48, and 94.3% for Random Forest, as evaluated through cross-validation. Integration of signal processing with traditional medicine holds potential to enhance diagnostic precision and alleviate healthcare burdens, offering a pathway for synergistic advancements in healthcare systems. Implementation of signal processing techniques in Unani medicine could streamline disease diagnosis, leading to more effective patient management and healthcare resource allocation. Integrating modern diagnostics with traditional medicine practices may foster greater acceptance and accessibility of alternative healthcare approaches, promoting holistic well-being within communities. This study pioneers the application of signal processing methods to pulse signals within Unani medicine, offering a novel approach to enhance disease diagnosis and advance traditional medical systems.
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