The need to secure the security and privacy of medical health information is elevated as the volume of highly sensitive patient data grows due to the boost in digitization of healthcare facilities. Traditional healthcare systems have struggled to protect patient data against tampering, breaches, and unauthorized access. Blockchain technology is a robust platform improving data security and privacy in the healthcare industry, given its decentralized and immutable characteristics. In keeping with the privacy of patient data and ensuring integrity, the diagnosis illness was diabetes mellitus and hypertension. Different machine learning approaches, particularly Random Forest , Support Vector Machines , Decision Tree , Naïve Bayes , and K-Nearest Neighbors, were applied for accurate illness identification. This research elucidates how to utilize blockchain technology to improve a tampering-free framework for storing and sending insecure diagnostic predictions and additionally created patient data. Similarly, managed by smart contracts and algorithms is the predicted diagnostic results and newly gathered patient information which are only accessed by authorized persons which are medical professionals. This research aim to increase the efficiency of disease detection in healthcare analytics while prioritizing patient data protection and security.

The need to secure the security and privacy of medical health information is elevated as the volume of highly sensitive patient data grows due to the boost in digitization of healthcare facilities. Traditional healthcare systems have struggled to protect patient data against tampering, breaches, and unauthorized access. Blockchain technology is a robust platform improving data security and privacy in the healthcare industry, given its decentralized and immutable characteristics. In keeping with the privacy of patient data and ensuring integrity, the diagnosis illness was diabetes mellitus and hypertension. Different machine learning approaches, particularly Random Forest , Support Vector Machines , Decision Tree , Naïve Bayes , and K-Nearest Neighbors, were applied for accurate illness identification. This research elucidates how to utilize blockchain technology to improve a tampering-free framework for storing and sending insecure diagnostic predictions and additionally created patient data. Similarly, managed by smart contracts and algorithms is the predicted diagnostic results and newly gathered patient information which are only accessed by authorized persons which are medical professionals. This research aim to increase the efficiency of disease detection in healthcare analytics while prioritizing patient data protection and security.

the healthcare industry, given its decentralized and immutable characteristics. In keeping with the privacy of patient data and ensuring integrity, the diagnosis illness was diabetes mellitus and hypertension. Different machine learning approaches, particularly Random Forest , Support Vector Machines , Decision Tree , Naïve Bayes , and K-Nearest Neighbors, were applied for accurate illness identification. This research elucidates how to utilize blockchain technology to improve a tampering-free framework for storing and sending insecure diagnostic predictions and additionally created patient data. Similarly, managed by smart contracts and algorithms is the predicted diagnostic results and newly gathered patient information which are only accessed by authorized persons which are medical professionals. This research aim to increase the efficiency of disease detection in healthcare analytics while prioritizing patient data protection and security.

The need to secure the security and privacy of medical health information is elevated as the volume of highly sensitive patient data grows due to the boost in digitization of healthcare facilities. Traditional healthcare systems have struggled to protect patient data against tampering, breaches, and unauthorized access. Blockchain technology is a robust platform improving data security and privacy in the healthcare industry, given its decentralized and immutable characteristics. In keeping with the privacy of patient data and ensuring integrity, the diagnosis illness was diabetes mellitus and hypertension. Different machine learning approaches, particularly Random Forest , Support Vector Machines , Decision Tree , Naïve Bayes , and K-Nearest Neighbors, were applied for accurate illness identification. This research elucidates how to utilize blockchain technology to improve a tampering-free framework for storing and sending insecure diagnostic predictions and additionally created patient data. Similarly, managed by smart contracts and algorithms is the predicted diagnostic results and newly gathered patient information which are only accessed by authorized persons which are medical professionals. This research aim to increase the efficiency of disease detection in healthcare analytics while prioritizing patient data protection and security.