CLOUD-BASED FRAMEWORK FOR EARLY DETECTION OF PARKINSON’S DISEASE AND COMORBIDITY PREDICTION USING MACHINE LEARNING, PRIVACY PRESERVATION

Abstract

An evolving neurodegenerative condition, Parkinson's disease is especially challenging to identify early and can sometimes be very much more so when symptoms closely mimic other illnesses. A novel approach to data collection from various global sources cloud-based and makes predictions of the symptomatology, in addition to incorporating privacy mechanisms for advancing comorbidity prediction with Parkinson's. This proposed system, proposed by this research, relies on geographically dispersed disease repositories that have been enriched with anonymized healthcare information gathered from diverse populations around the world. Advanced algorithms of machine learning are used to analyze cross-referenced symptom patterns with regional lifestyle factors, genetic predispositions and environmental influences, thus allowing for the early detection of PD and identification of co-occurring symptoms that may indicate the onset of other diseases. It will use sophisticated cryptography techniques like homomorphic encryption and differential privacy to protect all sensitive personal health information. It will use blockchain to ensure data integrity and provide the basis for a reliable, unalterable record system that allows the partners to build trust in all transactions. User privacy will be protected to satisfy international data protection standards when it facilitates a room for secure tracking and diagnosis of diseases in real time. This transforms global health as it focuses on equipping the healthcare clinician with an integrated decision-making system that could facilitate tailored care, initiate precursory early intervention mechanisms and catalyzes change thereby scaling up to make feasible an innovative solution for an inherently complex, cloud-based issue placed within the context of global health disparity.