Integrated Prediction System for Chronic Disease Diagnosis to Ensure Better Healthcare

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 the most common chronic diseases.