Examining the Impact of Data Imbalance on the Effectiveness of the Proposed Algorithm for Real-Time Prediction of Heart Disease and Suggesting Solutions
Authors:-Pragathi, Sneha Ghode, Nisha Hebbar, Bhoomika Surendra Naik, Professor Dr. Lokesh M R
Abstract-:The main goal of this review paper is to describe the impact that data imbalance has on the Prophet algorithm’s ability to accurately predict heart disease in real time and offer solutions for these effects. This case study of a mobile health illness management software includes three modules: User, Admin, and Doctor. ECG data is available to registered users, but they must also upload it in csv format so that the Prophet program, which generates educational reports, can further analyze it. The credibility of the predictions may be impacted by this biased data or noise, which could impair the model’s performance and lead to biases in its output. This review highlights some issues with the problem of imbalanced data, makes an effort to gather data and knowledge from the literature that is currently available, and offers some tactics that could be helpful in resolving such problems, including algorithm modification, data resampling, and synthetic data. The article concludes by discussing the application’s potential to improve heart disease early detection and streamline interactions between medical professionals and patients. Because it deals with healthcare quality, this review first offers a framework for future research on mobile health technologies and emphasizes the idea of addressing data imbalance in data-driven models.
