Heart Diseases Diagnosis Using Artificial Neural Networks

Information technology has virtually altered every aspect of human life in the present era. The application of informatics in the health sector is rapidly gaining prominence and the benefits of this innovative paradigm are being realized across the globe. This evolution produced large number of patients’ data that can be employed by computer technologies and machine learning techniques, and turned into useful information and knowledge. This data can be used to develop expert systems to help in diagnosing some life-threating diseases such as heart diseases, with less cost, processing time and improved diagnosis accuracy. Even though, modern medicine is generating huge amount of data every day, little has been done to use this available data to solve challenges faced in the successful diagnosis of heart diseases. Highlighting the need for more research into the usage of robust data mining techniques to help health care professionals in the diagnosis of heart diseases and other debilitating disease conditions. Based on the foregoing, this thesis aims to develop a health informatics system for the classification of heart diseases using data mining techniques focusing on Radial Basis functions and emerging Neural Networks approach. The presented research involves three development stages; firstly, the development of a preliminary classification system for Coronary Artery Disease (CAD) using Radial Basis Function (RBF) neural networks. The research then deploys the deep learning approach to detect three different types of heart diseases i.e. Sleep Apnea, Arrhythmias and CAD by designing two novel classification systems; the first adopt a novel deep neural network method (with Rectified Linear unit activation) design as the second approach in this thesis and the other implements a novel multilayer kernel machine to mimic the behaviour of deep learning as the third approach. Additionally, this thesis uses a dataset obtained from patients, and employs normalization and feature extraction means to explore it in a unique way that facilitates its usage for training and validating different classification methods. This unique dataset is useful to researchers and practitioners working in heart disease treatment and diagnosis. The findings from the study reveal that the proposed models have high classification performance that is comparable, or perhaps exceed in some cases, the existing automated and manual methods of heart disease diagnosis. Besides, the proposed deep-learning models provide better performance when applied on large data sets (e.g., in the case of Sleep Apnea), with reasonable performance with smaller data sets. The proposed system for clinical diagnoses of heart diseases, contributes to the accurate detection of such disease, and could serve as an important tool in the area of clinic support system. The outcome of this study in form of implementation tool can be used by cardiologists to help them make more consistent diagnosis of heart diseases

Detecting Heart Attacks Using Learning Classifiers

Cardiovascular diseases (CVDs) have emerged as a critical global threat to human life. The diagnosis of these diseases presents a complex challenge, particularly for inexperienced doctors, as their symptoms can be mistaken for signs of aging or similar conditions. Early detection of heart disease can help prevent heart failure, making it crucial to develop effective diagnostic techniques. Machine Learning (ML) techniques have gained popularity among researchers for identifying new patients based on past data. While various forecasting techniques have been applied to different medical datasets, accurate detection of heart attacks in a timely manner remains elusive. This article presents a comprehensive comparative analysis of various ML techniques, including Decision Tree, Support Vector Machines, Random Forest, Extreme Gradient Boosting (XGBoost), Adaptive Boosting, Multilayer Perceptron, Gradient Boosting, K-Nearest Neighbor, and Logistic Regression. These classifiers are implemented and evaluated in Python using data from over 300 patients obtained from the Kaggle cardiovascular repository in CSV format. The classifiers categorize patients into two groups: those with a heart attack and those without. Performance evaluation metrics such as recall, precision, accuracy, and the F1-measure are employed to assess the classifiers’ effectiveness. The results of this study highlight XGBoost classifier as a promising tool in the medical domain for accurate diagnosis, demonstrating the highest predictive accuracy (95.082%) with a calculation time of (0.07995 sec) on the dataset compared to other classifiers

An Optimized Recursive General Regression Neural Network Oracle for the Prediction and Diagnosis of Diabetes

Diabetes is a serious, chronic disease that has been seeing a rise in the number of cases and prevalence over the past few decades. It can lead to serious complications and can increase the overall risk of dying prematurely. Data-oriented prediction models have become effective tools that help medical decision-making and diagnoses in which the use of machine learning in medicine has increased substantially. This research introduces the Recursive General Regression Neural Network Oracle (R-GRNN Oracle) and is applied on the Pima Indians Diabetes dataset for the prediction and diagnosis of diabetes. The R-GRNN Oracle (Bani-Hani, 2017) is an enhancement to the GRNN Oracle developed by Masters et al. in 1998, in which the recursive model is created of two oracles: one within the other. Several classifiers, along with the R-GRNN Oracle and the GRNN Oracle, are applied to the dataset, they are: Support Vector Machine (SVM), Multilayer Perceptron (MLP), Probabilistic Neural Network (PNN), Gaussian NaEF;ve Bayes (GNB), K-Nearest Neighbor (KNN), and Random Forest (RF). Genetic Algorithm (GA) was used for feature selection as well as the hyperparameter optimization of SVM and MLP, and Grid Search (GS) was used to optimize the hyperparameters of KNN and RF. The performance metrics accuracy, AUC, sensitivity, and specificity were recorded for each classifier. The R-GRNN Oracle was able to achieve the highest accuracy, AUC, and sensitivity (81.14%, 86.03%, and 63.80%, respectively), while the optimized MLP had the highest specificity (89.71%)

Smart Detection of Cardiovascular Disease Using Gradient Descent Optimization

The Internet of Medical Things (IoMT) is the networking of health things or equipment that communicate data over the internet without the need for human involvement in the healthcare field. A large quantity of data is collected from numerous sensors in the health field, and it is all transferred and stored on the cloud. This data is growing bigger here all time, and it's becoming increasingly challenging to secure it on the cloud with real-time storage and computing. Data security problem can be addressed with the aid of machine algorithms and fog computing. For data security in IoMT gadgets correspondence in an intelligent fashion, an intelligent encryption algorithm (IEA) is proposed using blockchain technology in cloud based system framework (CBSF). It is applied on patient’s database to provide immutable security, tampering prevention and transaction transparency at the fog layer in IoMT.  The suggested expert system's results indicate that it is suitable for use in for the security. In the fog model, the blockchain technology approach also helps to address latency, centralization, and scalability difficulties

Backpropagation Neural Network Based on Local Search Strategy and Enhanced Multi-objective Evolutionary Algorithm for Breast Cancer Diagnosis

The role of intelligence techniques is becoming more significant in detecting and diagnosis of medical data. However, the performance of such methods is based on the algorithms or technique. In this paper, we develop an intelligent technique using multiobjective evolutionary method hybrid with a local search approach to enhance the backpropagation neural network. First, we enhance the famous multiobjective evolutionary algorithms, which is a non-dominated sorting genetic algorithm (NSGA-II). Then, we hybrid the enhanced algorithm with the local search strategy to ensures the acceleration of the convergence speed to the non-dominated front. In addition, such hybridization get the solutions achieved are well spread over it. As a result of using a local search method the quality of the Pareto optimal solutions are increased and all individuals in the population are enhanced. The key notion of the proposed algorithm was to  show a new technique to settle automaticly artificial neural network design problem. The empirical results generated by the proposed intelligent technique evaluated by applying to the breast cancer dataset and emphasize the capability of the proposed algorithm to improve the results. The network size and accuracy results of the proposed method are better than the previous methods. Therefore, the method is then capable of finding a proper number of hidden neurons and error rates of the BP algorithm

Various Feature Selection Techniques in Type 2 Diabetic Patients for the Prediction of Cardiovascular Disease

Cardiovascular disease (CVD) is a serious but preventable complication of type 2 diabetes mellitus (T2DM) that results in substantial disease burden, increased health services use, and higher risk of premature mortality [10]. People with diabetes are also at a greatly increased risk of cardiovascular which results in sudden death, which increases year by year. Data mining is the search for relationships and global patterns that exist in large databases but are `hidden' among the vast amount of data, such as a relationship between patient data and their medical diagnosis. Usually medical databases of type 2 diabetic patients are high dimensional in nature. If a training dataset contains irrelevant and redundant features (i.e., attributes), classification analysis may produce less accurate results. In order for data mining algorithms to perform efficiently and effectively on high-dimensional data, it is imperative to remove irrelevant and redundant features. Feature selection is one of the important and frequently used data preprocessing techniques for data mining applications in medicine. Many of the research area in data mining has improved the predictive accuracy of the classifiers by applying the various techniques of feature selection This paper illustrates, the application of feature selection technique in medical databases, will enable to find small number of informative features leading to potential improvement in medical diagnosis. It is proposed to find an optimal feature subset of the PIMA Indian Diabetes Dataset using Artificial Bee Colony technique with Differential Evolution, Symmetrical Uncertainty Attribute set Evaluator and Fast Correlation-Based Filter (FCBF). Then Mutual information based feature selection is done by introducing normalized mutual information feature selection (NMIFS). And valid classes of input features are selected by applying Hybrid Fuzzy C Means algorithm (HFCM)

SS-FD: Internet of medical things based patient health monitoring system

Internet of Medical Things (IoMT) consists of connected devices used to collect patient health information in a real-time environment. The IoMT device effectively handles medical issues by using health wearable and medical-grade wearables. Although IoMT can process the collected data, it has few pitfalls, such as interoperability of data, standardization issues, and computation complexity while detecting disease. By considering these issues, in this work, IoMT is utilized in the field of the remote patient monitoring system. Initially, the IoMT devices are placed on the human body and collect their health information continuously. The gathered details are processed using a salp swarm optimized fuzzy deep neural network (SS-FD). This system supports the patient health monitoring process with minimum low-cost consumption. The SS-FD classifier processes the obtained data; primary and emergency data is classified according to the fuzzy rule. This process improves the remote patient health data analysis and reduces the difficulties involved in the patient health analysis. Then the efficiency of the system is evaluated using experimental result