Prognosis Of Heart Disease Research Articles

Heart disease prediction using KNN algorithm approach

Machine getting to know and records-based totally techniques for predicting and diagnosing coronary heart ailment may be an extraordinary medical gain, but it's far a chief undertaking to improve. In many countries there may be a shortage of cardiovascular professionals and a giant quantity of instances of misdiagnosis may be addressed by way of setting up correct and powerful early-stage cardiac forecasts with the aid of medical decision-making analysis of virtual patient information. This has a look at aimed picking out excessive-overall performance devices getting to know variants for such diagnostic purposes. Several gadget-getting-to-know algorithms have been used which might be in comparison and done with accuracy and accuracy in predicting heart disease. The scores of the importance of each detail are restrained to all algorithms used except MLP and KNN. All elements are calculated based totally on the cost points to find those who provide high-danger coronary heart disease prognosis. The look discovered that using Kaggle's 3-segment cardiac database based on pro-k (KNN), choice tree (DT), and random Forests (RF) RF technique algorithms done ninety-seven.2% accuracy and 97.2% sensitivity to make clear. Therefore, we've observed that an easily supervised machine gaining knowledge of a set of rules can be wielded to make coronary heart disease conjecture with the very best accuracy and the most satisfactory possible use.

Surrogate Biomarker to Identify Obesity and Predict Cardiovascular Disease Risk: A Systematic Review

The study investigates the relationship between obesity and cardiovascular disease (CVD) risk, comparing the use of body mass index (BMI) versus other biomarkers in predicting CVD risk among obese individuals. It conducts a systematic literature review following the PRISMA guidelines, searching databases for relevant articles published from 2017-2022. The review analyzes 12 eligible articles and finds that factors beyond just BMI, such as genetics, physical activity, metabolic disorders, previous heart disease history, nutrition, fat distribution, and changes in BMI, can significantly impact the prognosis of heart disease in obese individuals. Importantly, the study shows that measures of fat distribution, like waist-to-height ratio, waist circumference, log-transformed body shape index (LBSIZ), and the ratio of visceral adipose tissue (VAT) to subcutaneous adipose tissue (SAT), are superior to BMI in predicting CVD risk among those with obesity. The key takeaway is that while obesity is strongly linked to CVD risk, BMI alone often fails to predict that risk accurately. Fat distribution measures may be a more effective tool for identifying obesity status and predicting associated CVD risk compared to relying solely on BMI.

Enhanced LSTM for heart disease prediction in IoT-enabled smart healthcare systems

Cardiac patients require prompt and effective treatment to prevent heart attacks through accurate prediction of heart disease. The prognosis of heart disease is complex and requires advanced knowledge and expertise. Healthcare systems are increasingly integrated with the internet of things (IoT) to collect data from sensors for diagnosing and predicting diseases. Current methods employ machine learning (ML) for these tasks, but they often fall short in creating an intelligent framework due to difficulties in handling high-dimensional data. A groundbreaking health system leverages IoT and an optimized long short-term memory (LSTM) algorithm, enhanced by the red deer (RD) algorithm, to accurately diagnose cardiac issues. Continuous monitoring of blood pressure and electrocardiograms (ECG) is conducted through heart monitor devices and smartwatches linked to patients. The gathered data is combined using a feature fusion approach, integrating electronic medical records (EMR) and sensor data for the extraction process. The RD-LSTM model classifies cardiac conditions as either normal or abnormal, and its performance is benchmarked against other deep-learning (DL) models. The RD-LSTM model showed better improvement in prediction accuracy over previous models.

BSANet: Boundary-aware and scale-aggregation networks for CMR image segmentation

The accurate segmentation of distinct cardiac regions from cardiac magnetic resonance (CMR) images is pivotal for enhancing the diagnosis and prognosis of heart diseases. However, the complex structure of the heart and the low contrast in CMR images pose significant challenges to achieving precise segmentation. In response to these difficulties, we propose a novel approach named BSANet, which integrates the inherent local characteristics of convolution with the global feature extraction of the Transformer through an elaborately designed multi-scale Boundary-Aware (MBA) module and a Scale-Aggregation TransFormer (SAT) module. The MBA module is specifically tailored to address the issue of blurry boundaries in CMR images. It enhances the edge extraction ability within low-level feature maps. On the other hand, the SAT module is designed to tackle the complexity and diversity of heart structures. This module retains crucial scale information to enhance segmentation performance while efficiently leveraging the computational capabilities of the Transformer via spatial reduction. Experimental results performed on three datasets show the superior performance of BSANet compared to the state-of-the-art methods. Notably, on the ACDC dataset, the proposed BSANet achieves the highest Dice score of 92.39%, while maintaining the lowest parameters. Furthermore, BSANet demonstrates optimal generalization performance on the RVSC and Sunnybrook datasets, underscoring its potential as a valuable tool for radiologists in clinical diagnosis.

Biochemical changes in the blood of domestic dogs with cardiomyopathy in the setting of obesity

In clinical settings, cardiac biomarkers with high sensitivity and specificity can be used to predict or diagnose heart disease. In humane clinical practice, troponins I and T have been used as predictors of heart disease, and the concentration of N-terminal pro-B-type natriuretic peptide (NT-proBNP) is also considered the most reliable predictor of heart disease. Veterinary cardiac biomarkers are not yet widely used by practitioners in the diagnosis of heart disease, but this does not negate the high informative value of specific cardiac parameters such as canine N-terminal pro-B-type natriuretic peptide (cNT-proBNP) and canine cardiac troponin I (ccTnI). Given the above, there is a need to conduct biochemical blood tests for cardiac pathology in dogs. The aim of the study was to determine specific parameters (cNT-proBNP and ccTnI) and blood lipidogram of domestic dogs with cardiomyopathy in the setting of obesity. The analysis of blood tests showed an increase in the content of ccTnI by more than 20 times (Р < 0.001) and cNT-pro BNP by 17.6 times (Р < 0.001) in domestic dogs with cardiomyopathy in the setting of obesity. According to the results of lipidogram determination, the dogs of the experimental group showed an increase in total cholesterol by 66.9 %, low-density lipoprotein (LDL) by 8.4 times, and very low-density lipoprotein (VLDL) by 4.2 times. The studies showed significant changes in blood biochemical parameters in cardiomyopathy in domestic dogs with obesity, which confirms the diagnostic value of these biomarkers. The research results deserve the attention of the veterinary community of practitioners and can be used in the diagnosis and subsequent prognosis of heart disease in the future.

Research progress on the improvement of cardiovascular diseases through the autonomic nervous system regulation of the NLRP3 inflammasome pathway.

Cardiovascular disease stands as a leading global cause of mortality. Nucleotide-binding Oligomerization Domain-like Receptor Protein 3 (NLRP3) inflammasome is widely acknowledged as pivotal factor in specific cardiovascular disease progression, such as myocardial infarction, heart failure. Recent investigations underscore a close interconnection between autonomic nervous system (ANS) dysfunction and cardiac inflammation. It has been substantiated that sympathetic nervous system activation and vagus nerve stimulation (VNS) assumes critical roles withinNLRP3 inflammasome pathway regulation, thereby contributing to the amelioration of cardiac injury and enhancement of prognosis in heart diseases. This article reviews the nexus between NLRP3 inflammasome and cardiovascular disorders, elucidating the modulatory functions of the sympathetic and vagus nerves within the ANS with regard to NLRP3 inflammasome. Furthermore, it delves into the potential therapeutic utility of NLRP3 inflammasome to be targeted by VNS. This review serves as a valuable reference for further exploration into the potential mechanisms underlying VNS in the modulation of NLRP3 inflammasome.

A Comparative Analysis using various algorithm Approaches to Enhance Heart Disease Prognosis

INTRODUCTION: Modern advancements in technology and data science have propelled the healthcare industry towards developing more accurate disease prognostic prediction models. Heart disease, being a leading cause of mortality globally, is a critical area of focus. This study delves into enhancing heart disease prognosis through a comprehensive exploration of various algorithmic approaches.
 OBJECTIVES: The objective of this paper is to compare and analyze different algorithmic techniques to improve heart disease prognosis using a dataset comprising data from over thirty thousand individuals obtained through Kaggle.
 METHODS: Techniques derived from social network analysis are employed to conduct this research. Data preprocessing, feature engineering, algorithm selection (including Stochastic Gradient Descent, AdaBoosting, Support Vector Machine, and Naive Bayes), hyperparameter tuning, model evaluation, and visualization are part of the systematic research process.
 RESULTS: The main results obtained in this paper include the identification of Naive Bayes as the most effective model for heart disease prognosis, followed by AdaBoosting, SVM, and Stochastic Gradient Descent. Performance evaluation metrics such as AUC, CA, F1, Precision, and Recall demonstrate the efficacy of these models.
 CONCLUSION: This research contributes to improving heart disease prognosis by leveraging algorithmic techniques and thorough analysis. The study envisions integrating the developed model into healthcare systems for widespread access to accurate heart disease prediction, with future plans to enhance data collection and model improvement for better outcomes.

Advanced Heart Disease Prediction: Deep Learning-Enhanced Convolutional Neural Network in the Internet of Medical Things Environment

Using the large dataset from the UCI Machine Learning Repository, this study presents a state-of-the-art Hybrid Convolutional Neural Network (HCNN) for predicting heart disease. The HCNN design has convolutional layers, leftover blocks, and attention methods. These help pull more features from cardiovascular health data, even when the patterns are very complex. Utilizing these deep learning parts, the HCNN shows better prediction skills, getting higher accuracy, strong classification (AUC), and a fair F1 Score. Because the model can change to find complicated connections in the information, it could be used to make medical diagnosis better. The HCNN is unique because it can instantly learn hierarchical structures from raw data. This lets it find hidden features that are important for accurately predicting heart disease. The convolutional layers help the model find local patterns, and the residue blocks stop problems with disappearing gradients, which makes training for deep designs more efficient. Attention processes make the network even more focused on important traits, which adds to its amazing ability to tell them apart. This work opens the door for researchers, doctors, and data scientists to use deep learning, and especially HCNN, to improve cardiovascular health analytics. This study takes a big step toward more accurate and efficient heart disease prognosis by giving a complete overview of the model's architecture and performance metrics. This opens the door for more research and use of advanced neural networks in the field of predictive medical diagnostics.

Artificial Intelligence and Machine Learning Algorithms in Modern Cardiology

BACKGROUND: Recent years have witnessed the widespread adoption of machine learning (ML) and deep learning techniques in various health-care applications. Artificial intelligence and ML algorithms using big medical data make it possible to predict diseases and enable the development of personalized treatments for patients. Heart diseases are one of the most common chronic diseases affecting human health, and early detection can reduce the mortality rate. AIM: We aimed to review different types of ML techniques and their applications in heart disease risk detection. METHODS: For different cardiovascular diseases, the choice of algorithms should be tailored based on their accuracy and efficiency RESULTS: The research presented highlights the critical global issue of heart disease and its impact on public health. The urgency to address this global problem is emphasized, as heart disease has become a significant factor in the increasing mortality rate worldwide. The introduction of ML in the prognosis of heart disease is a significant step toward realizing predictive, preventive, and personalized health care and reducing health-care costs. In this study, a comparative evaluation of ML models was made: Logistic regression, decision tree, random forest, and support vector machine. The quality of the data, as well as the choice of an appropriate algorithm, is key factors in the assessment of heart diseases. CONCLUSION: Despite the impressive performance of ML, there are doubts about its robustness in traditional health-care systems due to many security and privacy issues.

A heart disease prognosis pipeline for the edge using federated learning

Cloud computing and edge computing have revolutionized deployments by giving virtually unlimited computing and storage to ensure the scalability and availability of applications. This paper explores an application that can be used as a decision support system for heart disease prognosis. It discusses deployment strategies on a cloud-native model and an edge-optimized model. The application contains a customized prediction pipeline named ClassifyIT with a custom neural network architecture called IPANN, supported by a feature selector named MIST-CC and a regularizer named STIR. ClassifyIT was observed to give an accuracy of 87.16% on the Cleveland dataset, compared to 78.80% for a regular deep network. The addition of the MIST-CC feature selection algorithm to the deep network was shown to improve its accuracy to 81.97%, and it is further enhanced to 85.54% by adding STIR. This pipeline is then deployed on an application based on a cloud-native architecture that uses microservices. The design is expanded to an edge-optimized architecture that improves scalability by moving part of the computation to the user device. The machine learning pipeline is further enhanced using federated learning to improve localization and collaborative learning. Both architectures are compared in a subjective fashion based on various parameters.

Diagnosis of heart diseases: A fuzzy-logic-based approach.

Cardiovascular diseases (CVD) also known as heart disease are now the leading cause of death in the world. This paper presents research for the design and creation of a fuzzy logic-based expert system for the prognosis and diagnosis of heart disease that is precise, economical, and effective. This system entails a fuzzification module, knowledge base, inference engine, and defuzzification module where seven attributes such as chest pain type, HbA1c (Haemoglobin A1c), HDL (high-density lipoprotein), LDL (low-density lipoprotein), heart rate, age, and blood pressure are considered as input to the system. With the aid of the available literature and extensive consultation with medical experts in this field, an enriched knowledge database has been created with a sufficient number of IF-THEN rules for the diagnosis of heart disease. The inference engine then activates the appropriate IF-THEN rule from the knowledge base and determines the output value using the appropriate defuzzification technique after the fuzzification module fuzzifies each input depending on the appropriate membership function. Moreover, the fusion of web-based technology makes it suitable and cost-effective for the prognosis of heart disease for a patient and then he can take his decision for addressing the problem based on the status of his heart. On the other hand, it can also assist a medical practitioner to reach a more accurate conclusion regarding the treatment of heart disease for a patient. The Mamdani inference method has been used to evaluate the results. The system is tested with the Cleveland dataset and cross-checked with the in-field dataset. Compared with the other existing expert systems, the proposed method performs 98.08% accurately and can make accurate decisions for diagnosing heart diseases.

A Novel Heart Disease Prediction System using Deep Multi-Layer Perceptron and Optimal Feature Selection Mechanism

Diagnosis and prognosis of heart disease (HD) are essential medical tasks for a correct classification, which helps cardiologists to treat the patient properly. The current medical system is unable to obtain the entire information from the heart disease database. It is difficult for a physician to analyze and diagnose chronic disease because it is a challenging endeavor. Hence this paper proposes a novel weight and bias tune deep multi-layer perceptron for heart disease prediction (WBTDMLP) with optimal feature selection using modified random forest (MRF). The proposed system comprised ‘3’ phases such as data preprocessing, feature selection, and HD prediction. Initially the HD prediction data is collected from the Cleveland dataset and the missing value imputation and data normalization is applied on the dataset to preprocess the dataset. Following that, the feature selection was performed by using the MRF algorithm. Finally, the HD prediction is done based on WBTDMLP approach and the parameters are tuned by Sobel sequence with Brownian random walk-based dragonfly optimization algorithm (SSBRWDOA). The results indicate that the proposed approach reaches 97.89% accuracy, which is relatively higher than existing methods.

Classification models combined with Boruta feature selection for heart disease prediction

Cardiovascular disease (CVD), generally called heart illness, is a collective term for various ailments that affect the heart and blood vessels. Heart disease is a primary cause of fatality and morbidity in people worldwide, resulting in 18 million deaths per year. By identifying those who are most vulnerable to heart diseases and ensuring they receive the appropriate care, premature demise can be prevented. Machine learning algorithms are now crucial in the medical field, especially when using medical databases to diagnose diseases. Such efficient algorithms and data processing techniques are applied to predict various diseases and offer much potential for accurate heart disease prognosis. Therefore, this study compares the performance logistic regression, decision tree, and support vector machine (SVM) methods with and without Boruta feature selection. The Cleveland Clinic Heart Disease Dataset acquired from Kaggle, which consists of 14 features and 303 instances, was used for the investigation. It was found that the Boruta feature selection algorithm, which selects six of the most relevant features, improved the results of the algorithms. Among these classification algorithms, logistic regression produced the most efficient result, with an accuracy of 88.52 %.

Downregulation of B4GALT5 attenuates cardiac fibrosis through Lumican and Akt/GSK-3β/β-catenin pathway

Virtually all forms of cardiac disease exhibit cardiac fibrosis as a common trait, which ultimately leads to adverse ventricular remodeling and heart failure. To improve the prognosis of heart disease, it is crucial to halt the progression of cardiac fibrosis. Protein function is intricately linked with protein glycosylation, a vital post-translational modification. As a fundamental member of the β1,4-galactosyltransferase gene family (B4GALT), β1,4-galactosyltransferase V (B4GALT5) is associated with various disorders. In this study, significant levels of B4GALT5 expression were observed in cardiac fibrosis induced by transverse aortic constriction (TAC) or TGFβ1 and the activation of cardiac fibroblasts (CFs). Subsequently, by administering AAV9-shB4GALT5 injections to TAC animals, we were able to demonstrate that in vivo B4GALT5 knockdown decreased the transformation of CFs into myofibroblasts (myoFBs) and reduced the deposition of cardiac collagen fibers. In vitro tests revealed the same results. Conversely, both in vivo and in vitro experiments indicated that overexpression of B4GALT5 stimulates CFs activation and exacerbates cardiac fibrosis. Initially, we elucidated the primary mechanism by which B4GALT5 regulates the Akt/GSK-3β/β-catenin pathway and directly interacts with laminin, thereby affecting cardiac fibrosis. Our findings demonstrate that B4GALT5 promotes cardiac fibrosis through the Akt/GSK-3β/β-catenin pathway and reveal laminin as the target protein of B4GALT5.

Heart Disease Prediction Classification using Machine Learning

Heart disease is a leading cause of mortality worldwide, and early detection and accurate prediction of heart disease can significantly improve patient outcomes. Machine learning techniques have shown great promise in assisting healthcare professionals in diagnosing and predicting heart disease. The diagnosis and prognosis of heart disease must be improved, refined, and accurate, because a small mistake can cause weakness or death. According to a recent World Health Organization study, 17.5 million people die each year. By 2030, this number will increase to 75 million.[2] This document explains how to enable online KSRM capabilities. The KSRM smart system allows users to report heart-related problems. This research paper aims to explore the use of machine learning algorithms for effective heart disease prediction classification with Ada boost for improve the accuracy of algorithm.

An efficient hybrid optimization algorithm for detecting heart disease using adaptive stacked residual convolutional neural networks

Heart disease remains a leading global cause of mortality. Cardiovascular disease is the most common vascular disorder contributing to heart-related issues. Critical factors impacting cardiovascular health include blood pressure, glucose control, and cholesterol management. The escalating mortality rates associated with heart disease underscore the urgency for precise and secure diagnoses to facilitate timely intervention and prevention. Incorrect diagnoses of cardiovascular disease can have devastating consequences, highlighting the critical need for accurate assessments. In this study, we introduce an intelligent system for heart disease prediction based on the integration of the ACLS-RCNN and ICSOA techniques. The workflow begins with the collection and preprocessing of patient information. Data preprocessing focuses on addressing missing values, employing IQR-RS scaling, and handling imbalanced data using the ROS technique. Next, features are extracted from the pre-processed data utilizing kernel-based linear discriminate analysis. To reduce the dimensionality of the dataset, hybrid optimization (CI-AO + GI-SMO) is employed to select informative attributes. Disease prognoses are then generated through the application of the ACLS-RCNN model, with further enhancement achieved by the Improved Cuttlefish-Swarm Optimisation Algorithm (ICSOA). Through extensive Python-based simulations, we evaluate the recommended system's performance in terms of accuracy, precision, recall, and f1-score, comparing it against established methodologies. Our software demonstrates remarkable accuracy in clinical data prediction and diagnosis, offering a promising tool for enhancing heart disease prognosis and early detection.

Investigation of the relationship between modified Glasgow prognostic score and no-reflow phenomenon in patients undergoing primary percutaneous coronary intervention for ST-elevation myocardial infarction

Objectives: No-reflow phenomenon (NRP) is a complication associated with poor clinical outcome in patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (pPCI). The modified Glasgow prognostic score (mGPS) is a novel immune-inflammatory index, derived from C-reactive protein (CRP) and serum albumin levels and has been shown to be associated with prognosis in heart disease. In this study we aimed to investigated the relationship between mGPS and NRP in patients undergoing pPCI for STEMI. Methods: A total of 379 patients (aged 59 ± 9.9 years; 54.9% male) were enrollled. The patients were divided into 2 groups:no-reflow (n = 72) and reflow (n = 307). No-reflow was defined as thrombolysis in myocardial infarction (TIMI) ≤ 2 flow. The mGPS of all patients was calculated from blood samples at admission. Logistic regression analysis was performed to determine the independent predictive factors for NRP. Results: Mean age, pain to balloon duration, troponin T, white blood cell (WBC), Syntax score, neutrophil to lymphocyte ratio (NLR), glucose level, C-reactive protein level (CRP), diabetic and female patient ratio were higher, while left ventricular ejection fraction, ST segment resolution ratio at 60 min, and serum albumin level were lower in the NRP group.Logistic regression analysis showed that WBC count [Hazard ratio (HR): 0.816, 95% confidence interval (CI): 0.728-0.914, p < 0.001], NLR (HR: 0.482, CI: 0.355-0.654, p < 0.001), pain-to-balloon time (HR: 0.976, CI:0.960-0.991, p = 0.002) and mGPS (HR: 3.213, CI: 1.643- 6.283, p = 0.001) were independent predictive factors for NRP. Conclusions: Modified GPS is an independent predictive factor for NRP in patients undergoing pPCI for STEMI.

An Adaptive Technique to Predict Heart Disease Using Hybrid Machine Learning Approach

cardiovascular disease is amongby far prevalent fatalities in today's society. Cardiovascular disease is extremely hard to predict using clinical data analysis. Machine learning (ML) hasproved to be useful for helping in judgement and predictions with the enormous amount data produced by the healthcare sectorbusiness. Furthermore, latest events in other IoT sectors have demonstrated that machine learning is used (IOT). Several studies have examined the use of MLa heart disease prediction. In this research, we describe a novel method that, by highlighting essential traits, can improvethe precision of heart disease prognosis. Numerous data combinations and well-known categorization algorithms are used to create the forecasting models. Using a decent accuracy of 88.7%, we raise the level of playusing a heart disease forecasting approach that incorporates a88.7% absolute certainty in a combination random forest and linear model. (HRFLM).

A Systematic Review: Do the Use of Machine Learning, Deep Learning, and Artificial Intelligence Improve Patient Outcomes in Acute Myocardial Ischemia Compared to Clinician-Only Approaches?

Cardiovascular diseases (CVDs) present a significant global health challenge and remain a primary cause of death. Early detection and intervention are crucial for improved outcomes in acute coronary syndrome (ACS), particularly acute myocardial infarction (AMI) cases. Artificial intelligence (AI) can detect heart disease early by analyzing patient information and electrocardiogram (ECG) data, providing invaluable insights into this critical health issue. However, the imbalanced nature of ECG and patient data presents challenges for traditional machine learning (ML) algorithms in performing unbiasedly. Investigators have proposed various data-level and algorithm-level solutions to overcome these challenges. In this study, we used a systematic literature review (SLR) approach to give an overview of the current literature and to highlight the difficulties of utilizing ML, deep learning (DL), and AI algorithms in predicting, diagnosing, and prognosis of heart diseases. We reviewed 181 articles from reputable journals published between 2013 and June 15, 2023, focusing on eight selected papers for in-depth analysis. The analysis considered factors such as heart disease type, algorithms used, applications, and proposed solutions and compared the benefits of algorithms combined with clinicians versus clinicians alone. This systematic review revealed that the current ML-based diagnostic approaches face several open problems and issues when implementing ML, DL, and AI in real-life settings. Although these algorithms show higher sensitivities, specificities, and accuracies in detecting heart disease, we must address the ethical concerns while implementing these models into clinical practice. The transparency of how these algorithms operate remains a challenge. Nevertheless, further exploration and research in ML, DL, and AI are necessary to overcome these challenges and fully harness their potential to improve health outcomes for patients with AMI.

Heart Disease Prognosis Using D-GRU with Logistic Chaos Honey Badger Optimization in IoMT Framework

In recent years, heart disease has superseded several other contributory death factors. It is challenging to predict an individual’s risk of acquiring heart disease since it requires both expert knowledge and real-world experience. Developing an effective method for the prognosis of heart disease using Internet of Medical Things (IoMT) technology in healthcare organizations by collecting sensor data from patients’ bodies, utilizing robust expert systems, and incorporating vast healthcare data on cardiac disorders to alert physicians in critical situations is a challenging task. Several machine learning-based techniques for predicting and diagnosing cardiac disease have recently been demonstrated. However, these algorithms cannot effectively handle high-dimensionalinformation due to the need for an intelligent framework incorporating multiple sources to predict cardiac illness. This work proposes a unique model for heart disease prediction based on deep learning, Deep Gated Recurrent Units (D-GRU), which combines with Stacked Auto Encoders. A novel optimization algorithm, the Logistic Chaos Honey Badger Algorithm, is proposed for optimal feature selection. Publicly available heart disease-related datasets collected from UCI Repository, Cleveland Database, are used for training the proposed D-GRU model. The trained model is further tested on the data gathered from the sensors in the IoMT framework. The performance of the proposed model is compared against several deep learning models and existing works in the literature. The proposed D-GRU model outperforms the other models taken for comparison andexhibits performance supremacy with an accuracy of 95.15% in predicting heart diseases.