Integrated Prediction System for Chronic Disease Diagnosis to Ensure Better Healthcare

Krishna Prasad K,Geetha Poornima K

doi:10.47992/ijhsp.2581.6411.0053

Abstract

Technology innovation has made life easy for human beings. Technology is being used everywhere. This also extends to the healthcare sector. The healthcare sector produces a large amount of data each minute. Because of privacy issues, much of the data generated is not used and is not publicly accessible. Healthcare data comes from diverse sources hence it will be always varied in nature. Keeping track of such data has become much easier these days. Predictive analysis in healthcare is an emerging technology that identifies the person with poor health where the risks of developing chronic conditions are more likely and provide better solutions in the field of healthcare. Statistical methods and algorithms can be used to predict the disease before the actual symptoms are revealed in humans. By using data analytics algorithms one can easily predict chronic diseases such as obesity, high/low Blood Pressure, diabetes, asthma, cardiopulmonary disorders. Because of an unhealthy diet, lack of proper exercise, stress, consumption of tobacco, alcohol, etc. chronic diseases are most common these days. If the symptoms of chronic diseases are detected in the early stages, there will be less risk of hospitalization by cost-effectively maintaining better health. Big data analysis and health care can be mixed to produce accurate results. The application of predictive analytics in healthcare is highlighted in this paper. It provides a broader analysis in the prevention of different chronic diseases by using predictive analytics. The paper also includes various issues that arise when handling health care data. For each chronic disease, diverse models, techniques, and algorithms are used for predicting and analyzing. The paper comprises a conceptual model that integrates the prediction of most common chronic diseases

Highlights

This paper highlights the objectives of the study, General idea of Predictive Analytics, PA and healthcare data, Prevention of chronic diseases using PA, Conceptual model for PA, use cases of PA, Challenges in PA
ABOUTPREDICTIVE ANALYTICS : PA is a branch of analysis that uses historical data and all the emerging technologies such as IoT, AI, Machine Learning, Deep Learning, etc. of the purpose of decision making
Wearable devices and EHRs have made the patient-related data omnipresent. Jobs such as scheduling appointments, waiting for health reports, bringing clinical records to hospitals for every visit, routine check-ups such as BP, Blood-Sugar test, checking the temperature, and other tests are removed as real-time data are available. It would be simpler for both patients and doctors if a single model is used to examine the most common chronic diseases

Summary

Introduction

These days, data are in tremendous demand. Each click on the Internet, every heartbeat of a patient on a monitor, every transaction of a customer produces large amount of data that is of some use someone. Predictive Analytics (PA) is a branch of analytics that uses historical data and Chronic diseases, EHR, Clinical data, EDW, different emerging technologies such as Artificial Intelligence, Machine Learning, Data Analytics, etc. Healthcare is of vital importance where algorithms can be used to gain insights into patients' diagnoses and identify reliability bottlenecks. With the help of data analytics algorithms, PA provides automated solutions that predict future outcomes for patients in the case of ChrDs. Healthcare costs are becoming highly unaffordable these days. If the risks of getting into chronic situations are predicted early, the long-term sufferings of patients can be avoided [1]. This paper highlights the objectives of the study, General idea of Predictive Analytics, PA and healthcare data, Prevention of chronic diseases using PA, Conceptual model for PA, use cases of PA, Challenges in PA

Objectives

Methods

Discussion

Conclusion