• Dr.S.RAJESH
• R Saranya
• M Sneha

Disease risk assessment systems are thought to have a great deal of potential to assist future smart cities and communities with their medical treatment issues because they can extract disease risk factors from a variety of patient characteristics, provide doctors with access to diagnostic resources, and reduce the amount of time that patients must undergo medical treatment. Disease risk assessment services are still unable to grow due to substantial obstacles including data security and privacy. In this paper, we present an efficient and Privacy-Preserving Disease Risk Assessment Approach named CARAD, a practical and confidential method based on Logistic Regression for evaluating disease risk in healthcare datasets. A healthcare provider can utilize CARAD to securely train a disease risk prediction model using different healthcare data from numerous hospitals and give users (such as patients and clinicians) disease risk prediction services while maintaining their privacy. During the stages of illness risk prediction and model training, all sensitive data is handled over ciphertexts without decryption. Private information of both users and healthcare providers can be effectively protected as a result.

Disease Risk Analysis, Security, Secure data Training, Logistic Regression, Privacy-preserving

"An Efficient and Privacy-Preserving Disease Risk Assessment using Logistic Regression", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 7, page no.j122-j131, July-2023, Available :http://www.jetir.org/papers/JETIR2307917.pdf

"An Efficient and Privacy-Preserving Disease Risk Assessment using Logistic Regression", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 7, page no. ppj122-j131, July-2023, Available at : http://www.jetir.org/papers/JETIR2307917.pdf