Daily Archives: April 17, 2025

Next-Gen Health Solutions
Authors:-Assistant Professor Priti Bharambe, Vikas Mahandule, Shraddha Phulsundar, Priti Aivale, Shivanjali Shinde

Abstract-The integration of Information Technology (IT) in healthcare has transformed patient care, data management, and operational efficiency. This paper explores the role of IT solutions in enhancing healthcare delivery, focusing on electronic health records (EHRs), telemedicine, artificial intelligence (AI), and blockchain technology. IT innovations facilitate real-time data sharing, improve diagnostic accuracy, and enable remote patient monitoring, thereby increasing accessibility and reducing costs. Despite significant advancements, challenges such as data safety, compatibility, and ethical concerns persist. This study examines both the advantages and limitations of IT-driven healthcare, proposing strategies to optimize its implementation. By leveraging technology effectively, healthcare systems can enhance patient results and overall efficiency, clearing the path for a more connected and intelligent healthcare ecosystem.

DOI: 10.61137/ijsret.vol.11.issue2.346

Published by: Kajal Tripathi

A Machine Learning Approach to Heart Disease Prediction: 5-Fold Cross Validation and Hyperparameter Optimization
Authors:-Dr.N.Chandrasekhar

Abstract-The primary objective of this research is to develop an effective predictive model for heart disease using various Machine Learning (ML) algorithms. In this study, four different ML models—Gradient Boosting (GB), Random Forest (RF), LightGBM (LGBM), and AdaBoost (AB)—were implemented and evaluated for their prediction accuracy. To ensure the reliability and generalization of the models, 5-fold cross-validation was applied along with Grid Search Cross Validation (Grid Search CV) for hyperparameter tuning. This technique helped in identifying the optimal parameters for each algorithm, thereby improving their performance. Among all the models, Gradient Boosting achieved the highest accuracy of 95.08%, followed by Random Forest and LightGBM, both with 91.80%, and AdaBoost with 90.16%. These results highlight the effectiveness of ensemble-based ML models, particularly Gradient Boosting, in accurately predicting the risk of heart disease.

DOI: 10.61137/ijsret.vol.11.issue2.345

Published by: Kajal Tripathi

The Impact of Personalization on E-commerce Conversion Rates: An Empirical Analysis of 100 Respondents
Authors:-Hitesh Ramdasani

Abstract- This study investigates the impact of personalization strategies on e-commerce conversion rates, examining the perceptions of 100 online shoppers. Through a simulated survey approach, the research explores the relationship between the level of perceived personalization experienced by consumers and their likelihood of making a purchase. The findings from the simulated data analysis suggest a positive correlation between higher levels of perceived personalization and increased conversion likelihood. These results underscore the importance of implementing effective personalization techniques for e-commerce businesses seeking to enhance customer engagement and drive sales.

DOI: 10.61137/ijsret.vol.11.issue2.344

Published by: Kajal Tripathi