Cardiovascular diseases, Congestive Heart Failure in particular, are a leading cause of deaths worldwide. Congestive Heart Failure has high mortality and morbidity rates. The key to decreasing the morbidity and mortality rates associated with Congestive Heart Failure is determining a method to detect high-risk individuals prior to the development of this often-fatal disease. Providing high-risk individuals with advanced knowledge of risk factors that could potentially lead to Congestive Heart Failure, enhances the likelihood of preventing the disease through implementation of lifestyle changes for healthy living. When dealing with healthcare and patient data, there are restrictions that led to difficulties accessing and obtaining data that have slowed down the ability to apply machine learning and data analytics to problems within the healthcare industry. As data access was limited, this research utilized previous studies and Natural Language Processing to discover common and successful methods to predicting aspects of Congestive Heart Failure. Additionally, this research will look at the most common models used from previous studies and some that have not been applied previously on a common data set used for heart disease analysis. This research will show that the same few types of data or datasets and models have been primarily used over the years and little advancements in analysis for Congestive Heart Failure have been made. Although these methods and datasets have not branched out there is promising results when attempting to predict Congestive Heart Failure on common datasets. With outlining what other data or models could be used this could finally lead to advancements to not only predicting Congestive Heart Failure but utilizing machine learning across the healthcare industry.

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License