Detection of Ischemic Heart Diseases from Medical Images

Pugazhenthi, D.; Meenakshi, Meenakshi

doi:10.1109/icmete.2016.97

Cited by 5 publications

(1 citation statement)

References 9 publications

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“…RNN is said to be most appropriate for sequentially featured data and data that is repetitive. In the realm of sequence-oriented tasks, the efficiency of the many methods operating on both of these forms of LSTM that Hochreiter and Schmidhuber developed [20] is rather outstanding.…”

Section: Deep Learning (Dl) For Forecasting Of Cardiovascular-related...mentioning

confidence: 99%

Untitled

2024

INDJCSE

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Heart disease is one of the most challenging tasks in health care. Several works have been proposed to address this challenge. In the recent past, some of the research articles are focused on the design of different machine learning algorithms for heart disease in health care, in this paper, we present an overview of supervised, unsupervised, and reinforcement learning algorithms used in the prediction of heart disease wherever possible. This review paper classifies some machine learning algorithms into different categories named Naïve Bays, Decision trees, support vectors, KNN, random forest, genetic algorithms, and structures with their relative performances. This paper also compares different algorithm techniques for heart diseases against accuracy and reliability.

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Section: Deep Learning (Dl) For Forecasting Of Cardiovascular-related...mentioning

confidence: 99%

Untitled

2024

INDJCSE

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A multi-variate heart disease optimization and recognition framework

Balaha

Shaban

El-Gendy

et al. 2022

Neural Comput & Applic

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Cardiovascular diseases (CVD) are the most widely spread diseases all over the world among the common chronic diseases. CVD represents one of the main causes of morbidity and mortality. Therefore, it is vital to accurately detect the existence of heart diseases to help to save the patient life and prescribe a suitable treatment. The current evolution in artificial intelligence plays an important role in helping physicians diagnose different diseases. In the present work, a hybrid framework for the detection of heart diseases using medical voice records is suggested. A framework that consists of four layers, namely “Segmentation” Layer, “Features Extraction” Layer, “Learning and Optimization” Layer, and “Export and Statistics” Layer is proposed. In the first layer, a novel segmentation technique based on the segmentation of variable durations and directions (i.e., forward and backward) is suggested. Using the proposed technique, 11 datasets with 14,416 numerical features are generated. The second layer is responsible for feature extraction. Numerical and graphical features are extracted from the resulting datasets. In the third layer, numerical features are passed to 5 different Machine Learning (ML) algorithms, while graphical features are passed to 8 different Convolutional Neural Networks (CNN) with transfer learning to select the most suitable configurations. Grid Search and Aquila Optimizer (AO) are used to optimize the hyperparameters of ML and CNN configurations, respectively. In the last layer, the output of the proposed hybrid framework is validated using different performance metrics. The best-reported metrics are (1) 100% accuracy using ML algorithms including Extra Tree Classifier (ETC) and Random Forest Classifier (RFC) and (2) 99.17% accuracy using CNN.

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