A Smart Healthcare Monitoring System for Heart Disease Prediction Based On Ensemble Deep Learning and Feature Fusion

Although mesh-connected computers are used almost exclusively for low-level local image processing, they are also suitable for higher level image processing tasks We illustrate this by presenting new optimal (in the O-notational sense) algorithms for computing several geometric properties of figures For example, given a black/white picture stored one pixel per processing element in an n × n mesh-connected computer, we give ?(n) time algorithms for determining the extreme points of the convex hull of each component, for deciding if the convex hull of each component contains pixels that are not members of the component, for deciding if two sets of processors are linearly separable, for deciding if each component is convex, for determining the distance to the nearest neighboring component of each component, for determining internal distances in each component, for counting and marking minimal internal paths in each component, for computing the external diameter of each component, for solving the largest empty circle problem, for determining internal diameters of components without holes, and for solving the all-points farthest point problem Previous mesh-connected computer algorithms for these problems were either nonexistent or had worst case times of ?(n2) Since any serial computer has a best case time of ?(n2) when processing an n × n image, our algorithms show that the mesh-connected computer provides significantly better solutions to these problems

Geometric algorithms for digitized pictures on a mesh-connected computer

Nowadays, machine learning algorithms have become very important in the medical sector, especially for diagnosing disease from the medical database. Many companies using these techniques for the early prediction of diseases and enhance medical diagnostics. The motivation of this paper is to give an overview of the machine learning algorithms that are applied for the identification and prediction of many diseases such as Naive Bayes, logistic regression, support vector machine, K-nearest neighbor, K-means clustering, decision tree, and random forest. In this work, many previous studies were reviewed that used machine learning algorithms for detecting various diseases in the medical area in the last three years. A comparison is provided concerning these algorithms, assessment processes, and the obtained results. Finally, a discussion of the previous works is presented.

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The Role of Machine Learning Algorithms for Diagnosing Diseases

Detection of heart disease through early-stage symptoms is a great challenge in the current world scenario. If not diagnosed timely then this may become the cause of death. In developing countries where heart specialist doctors are not available in remote, semi-urban, and rural areas; an accurate decision support system can play a vital role in early-stage detection of heart disease. In this paper, the authors have proposed a hybrid decision support system that can assist in the early detection of heart disease based on the clinical parameters of the patient. Authors have used multivariate imputation by chained equations algorithm to handle the missing values. A hybridized feature selection algorithm combining the Genetic Algorithm (GA) and recursive feature elimination has been used for the selection of suitable features from the available dataset. Further for pre-processing of data, SMOTE (Synthetic Minority Oversampling Technique) and standard scalar methods have been used. In the last step of the development of the proposed hybrid system, authors have used support vector machine, naive bayes, logistic regression, random forest, and adaboost classifiers. It has been found that the system has given the most accurate results with random forest classifier. The proposed hybrid system was tested in the simulation environment developed using Python. It was tested on the Cleveland heart disease dataset available at UCI (University of California, Irvine) machine learning repository. It has achieved an accuracy of 86.6%, which is superior to some of the existing heart disease prediction systems found in the literature.

A decision support system for heart disease prediction based upon machine learning

Coronary artery disease (CAD) is one of the main causes of cardiac death around the world. Due to its significant impact on the society, early and accurate detection of CAD is essential. This study proposes a novel nested ensemble nu-Support Vector Classification (NE-nu-SVC) model which combines several traditional machine learning methods and ensemble learning techniques for effective diagnosis of CAD. We validated our model using two well-known CAD datasets (Z-Alizadeh Sani and Cleveland). To improve the performance of the model, we selected clinically significant features from the datasets using a genetic search algorithm. To further improve our results, we applied a multi-level filtering technique to balance the data using the ClassBlancer and Resample methods. Our base algorithm, nu-SVC, is performed using four well-known kernel functions (linear, polynomial, radial basis (RBF) and sigmoid). The proposed NE-nu-SVC model provided the highest accuracy of 94.66% and 98.60% to predict CAD entities in the Z-Alizadeh Sani and Cleveland CAD datasets, respectively. Our system can aid the clinicians to diagnose CAD accurately and may probably replace other invasive diagnostic techniques.

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NE-nu-SVC: A New Nested Ensemble Clinical Decision Support System for Effective Diagnosis of Coronary Artery Disease

Breast cancer is considered one of the leading causes of death among women. In morocco, the ministry of health reports over 40.000 new cases each year. When lifestyle can be a preventive pattern, early detection remains a factor of a huge impact on the mortality of the diseases. Machine learning (ML) algorithms offer an alternative to breast cancer standard techniques of prediction, or at least can assist radiologists in their reasoning flow and thus saving many females and some males from breast cancer biopsy. The present study represents a benchmarking of different ML models. The research applies and compares four machine learning algorithms (kNN, decision tree, Binary SVM, and Adaboost) to predict whether a patient has a malignant or a benign tumor. The machine learning techniques have been trained then tested on the Breast Cancer Wisconsin dataset. The datasets features are fed into feature selection model with Neighbourhood Components Analysis (NCA) to reduce the number of features and therefore decrease the complexity of the model. The predictive accuracy reached a 99.12% for the kNN model, the best predictive specificity obtained was 9S.S6% for the Binary SVM model and the highest predictive sensitivity obtained was up to one for both kNN and Adaboost models.

Classification of Patients with Breast Cancer using Neighbourhood Component Analysis and Supervised Machine Learning Techniques

In this paper, we propose a parallel algorithm for data classification, and its application for Magnetic Resonance Images (MRI) segmentation. The studied classification method is the well-known c-means method. The use of the parallel architecture in the classification domain is introduced in order to improve the complexities of the corresponding algorithms, so that they will be considered as a pre-processing procedure. The proposed algorithm is assigned to be implemented on a parallel machine, which is the reconfigurable mesh computer (RMC). The image of size (mxn) to be processed must be stored on the RMC of the same size, one pixel per processing element (PE).

https://zenodo.org/record/34874/files/ParallelCMeansAlgorithm.pdf

Parallel c-means algorithm for image segmentation on a reconfigurable mesh computer

Diabetes is considered as one of the deadliest and chronic diseases in several countries. All of them are working to prevent this disease at early stage by diagnosing and predicting the symptoms of diabetes using several methods. The motive of this study is to compare the performance of some Machine Learning algorithms, used to predict type 2 diabetes diseases. In this paper, we apply and evaluate four Machine Learning algorithms (Decision Tree, K-Nearest Neighbours, Artificial Neural Network and Deep Neural Network) to predict patients with or without type 2 diabetes mellitus. These techniques have been trained and tested on two diabetes databases: The first obtained from Frankfurt hospital (Germany), and the second is the well-known Pima Indian dataset. These datasets contain the same features composed of mixed data; risk factors and some clinical data. The performances of the experimented algorithms have been evaluated in both the cases i.e. dataset with noisy data (before pre-processing/some data with missing values) and dataset set without noisy data (after preprocessing). The results compared using different similarity metrics like Accuracy, Sensitivity, and Specificity gives best performance with respect to state of the art.

Predicting diabetes diseases using mixed data and supervised machine learning algorithms

Over the years, the use of artificial intelligence methods in medical analysis has flourished with the development of diagnostic tests and medical knowledge. Thus, the techniques of classification and detection have made much progress towards offering to medical experts and the doctors, organized and hierarchical systems to provide them with more information and knowledge for their daily function. Diagnostic assistance or diagnostic aid is an important tool for identifying abnormalities such as heart diseases. Having an appropriate model to increase the accuracy of the system, even in cases where inputs are generally low, is necessary. This paper aims to set up a medical diagnostic support system for the early detection of heart diseases, depending on cardiovascular risk factors to determine different clinical parameters useful for diagnosis. The validation of the suggested method is made using the sensitivity and specificity measures.

Fuzzy cardiovascular diagnosis system using clinical data

COVID-19 is an infectious disease-causing flu-like respiratory problem with various symptoms such as cough or fever, which in severe cases can cause pneumonia. The aim of this paper is to develop a rapid and accurate medical diagnosis support system to detect COVID-19 in chest X-ray images using a stacking approach combining transfer learning techniques and KNN algorithm for selection of the best model. In deep learning, we have multiple approaches for building a classification system for analyzing radiographic images. In this work, we used the transfer learning technique. This approach makes it possible to store and use the knowledge acquired from a pretrained convolutional neural network to solve a new problem. To ensure the robustness of the proposed system for diagnosing patients with COVID-19 using X-ray images, we used a machine learning method called the stacking approach to combine the performances of the many transfer learning-based models. The generated model was trained on a dataset containing four classes, namely, COVID-19, tuberculosis, viral pneumonia, and normal cases. The dataset used was collected from a six-source dataset of X-ray images. To evaluate the performance of the proposed system, we used different common evaluation measures. Our proposed system achieves an extremely good accuracy of 99.23% exceeding many previous related studies.

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Bouchaib Cherradi

Papers

Classification of Patients with Breast Cancer using Neighbourhood Component Analysis and Supervised Machine Learning Techniques

Parallel c-means algorithm for image segmentation on a reconfigurable mesh computer

Predicting diabetes diseases using mixed data and supervised machine learning algorithms

Fuzzy cardiovascular diagnosis system using clinical data

A Novel COVID-19 Diagnosis Support System Using the Stacking Approach and Transfer Learning Technique on Chest X-Ray Images.