Paper Key : IRJ************317
Author: Sai Sudeep Jinkala
Date Published: 12 Nov 2024
Abstract
Effective monitoring and prediction of health parameters are critical for managing chronic conditions and promoting overall well-being. This project applies data analysis and forecasting techniques to predict various health metrics, including blood pressure, using time series analysis. By employing the ARIMA (Auto Regressive Integrated Moving Average) model, which is well-suited for time-dependent data, we forecast future health readings based on historical trends. The project involves data cleaning, model fitting, and generating predictions for multiple health indicators. The ARIMA models ability to capture patterns and dependencies in time series data enables accurate forecasting, providing valuable insights for proactive healthcare management. The results are visualized to demonstrate how predictive data analysis can be leveraged for improving patient care, offering a tool for timely interventions and informed decision-making across different health metrics.
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