Hybrid K-Means and Support Vector Machine to Predict Heart Failure

Prevalence of Heart Failure (HF) is increasing globally every day in addition to many undiagnosed cases. Affecting at least 26 million people worldwide, this chronic condition can't be cured while it can be prevented. Over the years, researchers have come up with machine learning models to pred...

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Bibliographic Details
Published in2021 2nd International Conference on Smart Electronics and Communication (ICOSEC) pp. 1678 - 1683
Main Authors Saravanan, Shruti, Swaminathan, Krithika
Format Conference Proceeding
LanguageEnglish
Published IEEE 07.10.2021
Subjects
Clustering algorithms
Elbow Method
Heart
Heart Failure
Hyperparameter Tuning
K-Means Clustering
Machine learning
Machine learning algorithms
Null Model
Prediction algorithms
Predictive models
Silhouette Method
Support Vector Machine (SVM)
Support vector machines
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Summary:Prevalence of Heart Failure (HF) is increasing globally every day in addition to many undiagnosed cases. Affecting at least 26 million people worldwide, this chronic condition can't be cured while it can be prevented. Over the years, researchers have come up with machine learning models to predict HF in an attempt to help diagnosticians to be better equipped. This paper proposes a prediction by applying K-Means clustering algorithm on the UCI Heart Failure Clinical Records data, thereby classifying the entire data into six clusters. Further, Support Vector Machine (SVM) is used on this cluster data for prediction of HF in patients based on risk factors which was achieved with an accuracy of 93.33%. Tuning the hyperparameters using grid search enhanced the model predictions.
DOI:10.1109/ICOSEC51865.2021.9591738