Artificial Prognosis of Cardiac Disease using an NN: A Data-scientific Approach in Outlier Handling

Maitra, Shithi; Hosain, Tonmoy; Al-Sakin, Abdullah; Shah, Faisal Muhammad

doi:10.1109/eict48899.2019.9068847

Cited by 3 publications

(1 citation statement)

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“…In [13], To predict the presence of cardiac disease, this research suggests a three-layer, binary classifier based on neural networks (NNs). Univariate and bivariate exploratory data analysis were used in the filtering procedures that were used to create the feature space (EDA).…”

Section: Related Workmentioning

confidence: 99%

Heart disease classification using optimized Machine learning algorithms

Kadhim

Radhi

2023

ijcsm

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Early detection of heart disease is exceptionally critical to saving the lives of human beings. Heart attack is one of the primary causes of high death rates throughout the world, due to the lack of human and logistical resources in addition to the high costs of diagnosing heart diseases which plays a key role in the healthcare sector, this model is suggested. In the field of cardiology, patient data plays an essential role in the healthcare system. This paper presents a proposed model that aims to identify the optimal machine learning algorithm that can predict heart attacks with high accuracy in the early stages. The concepts of machine learning are used for training and testing the model based on the patient's data for effective decision-making. The proposed model consists of three stages, the first stage is patient data collection and processing, and the second stage is data training and testing using machine learning algorithms Random Forest, Support Vector Machines, K-Nearest Neighbor, and Decision Tree) that show The best classification (94.958 percent) with the Random Forest algorithm and the third stage is optimized the classification results using one of the hyperparameters optimization techniques random search that shows The best accuracy was (95.4 percent) obtained also with RF

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Section: Related Workmentioning

confidence: 99%