A multi-variate heart disease optimization and recognition framework

Abstract

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.

[Retracted] Ant Colony Optimization‐Enabled CNN Deep Learning Technique for Accurate Detection of Cervical Cancer

“…The back propagation method is used in order to affect an update to the weight of the MLP. MLP allows for the recognition of nonlinear datasets that are separable [ 26 ].…”

A concentrated machine learning-based classification system for age-related macular degeneration (AMD) diagnosis using fundus images

“…Contour analysis is a pivotal step in refining ROIs and identifying object boundaries within images 38 , 39 . Contours represent the boundaries of objects and offer valuable properties like area, perimeter, and centroid 40 . The centroid of an object within a contour is calculated using moments, which are mathematical descriptors of the shape and spatial distribution of an object.…”

An Ensemble Feature Optimization for an Effective Heart Disease Prediction Model

“…Existing research is reviewed in order to define the issue and focus of each investigation. The use of ML [9] by a cardiologist aid in the early diagnosis and treatment of illness [10]. There have been many models which have been presented for prediction like supportvector-machine (SVM), K-nearest-neighbor (KNN), decision-trees (DT), artificial-neural-networks (ANN), extreme gradient boosting (XGBoost), and hybrid model [11,12] combining ML and evolutionary computing algorithm each with its strengths and weaknesses.…”