Key result
Machine learning algorithms show varying heart disease prediction accuracy, requiring complex models integrating diverse data.
Why the study?
Heart disease diagnosis and treatment are complex and hindered by shortages of resources, and existing predictive models using data mining and machine learning have achieved only marginal success.
Machine learning techniques show promise in predicting heart disease, but current models achieve only marginal success and require more complex, geographically diverse data sources to improve early detection.
ML heart disease prediction shows only marginal success and should not yet change practice; leaves open the need for complex models with diverse data.
Heart disease is one of the major causes of life complicacies and subsequently leading to death. The heart disease diagnosisand treatment are very complex, especially in the developing countries, due to the rare availability of efficient diagnostic tools andshortage of medical professionals and other resources which affect proper prediction and treatment of patients. Inadequate preventivemeasures, lack of experienced or unskilled medical professionals in the field are the leading contributing factors. Although, largeproportion of heart diseases is preventable but they continue to rise mainly because preventive measures are inadequate. In today’sdigital world, several clinical decision support systems on heart disease prediction have been developed by different scholars to simplifyand ensure efficient diagnosis. This paper investigates the state of the art of various clinical decision support systems for heart diseaseprediction, proposed by various researchers using data mining and machine learning techniques. Classification algorithms such as theNaïve Bayes (NB), Decision Tree (DT), and Artificial Neural Network (ANN) have been widely employed to predict heart diseases,where various accuracies were obtained. Hence, only a marginal success is achieved in the creation of such predictive models for heartdisease patients therefore, there is need for more complex model
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Danial Kamran (2023) conducted a review in Heart disease. Machine learning and data mining techniques was evaluated on Prediction accuracy. Various machine learning algorithms have been widely employed to predict heart diseases with varying accuracies, highlighting the need for more complex models incorporating diverse data sources.
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