ArticleHeliyon2024
Heart disease prediction using ECG-based lightweight system in IoT based on meta-heuristic approach.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- An Intelligent IoT-Compatible Arrhythmia Detection System Using a Hybrid Vision Transformer-LSTM Framework.Cardiovascular engineering and technology · 2026Article
- Heart disease diagnosis and categorization from ECG signals using hybrid Fuzzy-CNN machine optimized by meta-heuristic algorithms.Scientific reports · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Annually, the proportion of individuals suffering from cardiovascular disease rises significantly. Heart attacks are the most prevalent and unpleasant illness among them. Heart disease (HD) diagnosis can be complicated when there are multiple symptoms. The growing popularity of wearable smart devices has increased the likelihood of providing the Internet of Things (IoT). However, one of the biggest obstacles to overcome in implementing the system under IoT is developing a lightweight model for cardiac diagnosis and categorization. In this paper, we have presented a two-step heart disease classification method. This method includes demarcation of classes with the help of optimized non-linear support vector machine technique in the first step and determining the modified fuzzy class in the second step. Initially, pre-processing is accomplished using the ECG signals to eliminate noise and improve signal smoothness. Subsequently, features such as PQRS wave, linear characteristics, and reciprocal information are extracted from pre-processed signals. At the classification stage, the two-stage learning system is used to classify cardiac arrhythmias. First, using the wild horse optimization (WHO) technique (WHO-sigmoid-TH-NL-demarcation), each class is subjected to a binary classification based on feature demarcation, thresholding, and weighting of the sigmoid function. The information from the first stage will be transferred into the subsequent stage for an equal number of heart disease classifications. In the second step, a TS fuzzy logic system optimized by the Giza Pyramids Construction (GPC) approach (GPC-TS-Fuzzy) is utilized to classify each signal. The MIT-BIH arrhythmia dataset is used to assess the suggested approach. In a comprehensive evaluation of the suggested method, performance metrics including "accuracy, sensitivity, and specificity" yielded average values of 98.58 %, 98.13 %, and 96.47 %, respectively. The MATLAB platform is utilized to accomplish the proposed methodology.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.