Characterization of Wrist Pulse Using Machine Learning Techniques

This project is focused on the integration of traditional Ayurvedic principles with forefront technology and machine learning techniques to develop a system that operates automatically for the diagnosis of diseases through pulse analysis. Its primary aim is the development of a non-invasive Nadi Pariksha system that can aid healthcare professionals in the identification of various medical conditions. This approach is designed to augment early disease detection and diagnosis, ultimately mitigating the mortality rates associated with delayed diagnoses. The system is primarily focused on the assessment of wrist pulses (Nadi) at three specific anatomical locations, denoted as ‘vata,’ ‘pitta,’ and ‘kapha,’ utilizing pulse sensors. These pulse readings are subjected to processing to extract crucial pulse characteristics such as shape, rate, and amplitude. Advanced signal processing methods, including amplification, filtering, and noise reduction, they are used to improve the quality of pulse data, ensuring the precision of measurements. A substantial dataset of human pulse samples is amassed, representing a diverse range of health conditions. This dataset serves as a critical component in training a machine learning model designed to discern patterns between pulse characteristics and various health conditions, thereby creating a predictive tool for disease diagnosis. The machine learning model undergoes rigorous training and evaluation, with the Random Forest algorithm emerging as the preferred choice due to its superior accuracy (86.4%) when compared to other models, such as XG Boost and Multi-SVM. Prior to real-world implementation, the system's accuracy and reliability necessitate thorough validation through clinical testing. Ethical considerations, encompassing patient privacy, data security, and informed consent, hold utmost significance to ensure the ethical application of this healthcare technology. The potential benefits of this system include early disease detection, reduced mortality rates, and regular monitoring of patients‘ health, making it a valuable tool in preventing delayed diagnoses.