Encrypting and Predicting Medical Diagnosis Data
Furqan Mohammed Faiz, Mohd Umar Farooq, Adnan Muhammed Faiz, Mohammad Pasha, Maniza Hijab · 2021
With the rapid development in technology across various branches, data security is the rising concern. Currently, in the healthcare sector, users can contact doctors/hospitals, study or analyze symptoms using wide range of applications and information available on internet, and also occasionally predict diseases. Individuals can easily drop by an appropriate portal and enter their symptoms which then subsequently applies machine learning algorithm to the mentioned data and finally calculate chances of disease incidence. By such a process one may encounter several problems., as these hardly employ any successful data protection services. Therefore, there is a need to maintain the privacy of the individual. Here we focus on designing a CatBoost model with homomorphic encryption for protecting the patient data to make it more secure. Thus finally providing encrypted prediction of diagnosis which is efficient and effective.