Evolution of Machine Learning to Predict Prognostic Outcomes in Patients Hospitalized with Congestive Heart Failure Using Random Forest Modelling Technique
DOI:
https://doi.org/10.52783/jns.v14.4195Keywords:
CHF, SVC, ECG, Naive Bayes, Random Forest, Artificial IntelligenceAbstract
One of the applications for which data mining tools are achieving success is disease diagnosis. Using Single Machine Learning Technique in the diagnosis of heart disease has been comprehensively investigated showing acceptable levels of accuracy Human heartbeat dynamics has been demonstrated to provide promising markers of Congestive Heart Failure (CHF). The main objective of this paper is to develop an Intelligent System using data analytics modelling technique and machine learning, namely, random forest SVC super vector classifier. Thus we propose to develop an application which can predict the vulnerability of a heart disease given basic symptoms like age, sex, pulse rate etc.. Heart failure (HF) is a complex clinical syndrome resulting from structural or functional impairment of ventricular filling or ejection of blood. Affecting over 64 million people globally, HF represents a significant burden on healthcare systems. This paper reviews current diagnostic strategies, classifications, treatment modalities, and emerging research trends in heart failure management, including pharmacological innovations and the use of artificial intelligence (AI) in predicting patient outcomes. The machine learning algorithm classifier has proven to be the most accurate and reliable algorithm and hence used in the proposed system.
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