Privacy preservation and data utility are two important key factors for publishing the data of multiple sensitive
attributes. Traditional anonymization methods often fail to protect against re-identification risks in complex datasets. The
proposed system uses semantic L-diversity technique to partition the data into the different buckets. Slicing technique is used to
partition the data into multiple sensitive tables along with the quasi table. This approach also uses a bucket id to keep the
associations among the sensitive tables and quasi table. Additionally, the framework provides a mechanism to generate and
securely re-identify summary tables derived from sensitive datasets, ensuring a balance between accessibility and confidentiality.
The system proposes a framework that ensures data utility while minimizing privacy risks. The system aims to provide a solution
for organizations to publish valuable data responsibly without compromising individual privacy
Bonumaddi Kumari, Chikkala Soujanya· International Journal for Re...· 0 citations
ABSTRACT:
The Vital Predict-Optimizer: Optimized Health Risk Prediction System is a Machine Learning-based healthcare application developed to predict the risk of multiple chronic diseases at an early stage. The system analyzes important health parameters such as age, Body Mass Index (BMI), blood pressure, glucose level, cholesterol level, heart rate, and lifestyle habits including smoking and alcohol consumption. Advanced data preprocessing techniques such as data cleaning, normalization, and feature selection are applied to improve data quality and prediction accuracy. Multiple machine learning algorithms are trained and optimized to enhance the overall performance of the prediction model. The system classifies the health risk into Low, Medium, and High categories based on the user's health profile. Optimization techniques are used to improve model efficiency, reduce prediction errors, and increase reliability. The application provides fast and accurate health risk assessments through a simple and user-friendly interface. It supports early disease detection, enabling users to take preventive healthcare measures before conditions become severe. The system also helps healthcare professionals in making better clinical decisions through intelligent risk analysis. Secure handling of sensitive health information ensures user privacy and data protection. Performance evaluation is carried out using metrics such as Accuracy, Precision, Recall, and F1-Score. The proposed system offers a reliable, scalable, and cost-effective solution for predictive healthcare. Overall, Vital Predict-Optimizer combines Artificial Intelligence, Machine Learning, and optimization techniques to deliver accurate, efficient, and personalized health risk prediction.Top of Form
Bonumaddi Kumari, Bangari Dinesh Kumar· International Scientific Jou...· 0 citations