Prediction of Type-II Diabetic through Blockchain Technology

Objectives: Blockchain technology is a recent development for extracting relevant information from the vast amount of data stored over cloud servers. The main objective of the present work is to use blockchain technology to predict diabetic patients at an early stage, and the proposed method improves data security and integrity. Methods: The proposed methodology consists of five key stages i.e. data collection, preprocessing, block formation, block evaluation, and finally valuable results are extracted. The source of data is related to diabetic patients which is taken from the Kaggle repository and categorized by age group. A blockchain model is developed and Python programming language is used to secure storage of the data, real-time analysis, and enhanced data security. Additionally, the study also finds association values between age groups and diabetic conditions which provide more meaningful information about affected people from diabetes. Findings: In this present work, statistical technique is used to evaluate the results and observe that the prevalence of diabetes increases with age, especially between the age group from 50 to 60. The study shows that blockchain may enhance chronic disease management by providing safe data storage and real-time analysis. The study combines blockchain technology with statistical correlation analysis, making it more suited for prediction rather than monitoring. Additionally, the storage size and retrieval time of the data are also computed in this study. Novelty: The primary contribution of the present work is that age-based block segmentation offers an innovative method for organizing and analysing patient data, especially for type-II diabetic, which have not been highlighted in other studies available in the literature. Keywords: Blockchain Technology, Medical Database, Diabetic Patients, Early Prediction, Data Security