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Cardiovascular disease (CVD) is a variety of diseases that affect the blood vessels and the heart. The authors propose a set of deep learning inspired by the approach used in CVD support centers for the early diagnosis of CVD using deep learning techniques. Data were collected from patients who received CVD screening. The authors propose a predicti...
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... forget gate ft regulates the data eliminated from the past long-term state ct -1 The output gate ot controls the development of the present short-term state ht utilizing the data from the present long-term state. The LSTM cell is outlined in Figure 1. We can describe it using the following: ...
Citations
... In their study, Oyewola et al. (14) utilized an ensemble optimization DL method to diagnose early CVD. They employed the Kaggle Cardiovascular Dataset for both training and testing purposes. ...
... Singh et al. (13), introduced an IoMT-Enhanced DL framework, incorporating a hybrid architecture combining CNNs and RNNs, extracting spatial and sequential features from heterogeneous IoMT data sources, and emphasizing interpretability and impact on treatment processes. Oyewola et al. (14) proposed an ensemble optimization DL technique that stands out for outperforming various NN architectures with high accuracy and simplifying CVD diagnosis for medical professionals. Elavarasi et al. (19) presented an ESA-integrated SVM for CVD prediction, focusing on interpretability through FS and optimizing FS using ESA and SVM while addressing challenges associated with traditional systems. ...
Introduction
Cardiovascular disease (CVD) stands as a pervasive catalyst for illness and mortality on a global scale, underscoring the imperative for sophisticated prediction methodologies within the ambit of healthcare data analysis. The vast volume of medical data available necessitates effective data mining techniques to extract valuable insights for decision-making and prediction. While machine learning algorithms are commonly employed for CVD diagnosis and prediction, the high dimensionality of datasets poses a performance challenge.
Methods
This research paper presents a novel hybrid model for predicting CVD, focusing on an optimal feature set. The proposed model encompasses four main stages namely: preprocessing, feature extraction, feature selection (FS), and classification. Initially, data preprocessing eliminates missing and duplicate values. Subsequently, feature extraction is performed to address dimensionality issues, utilizing measures such as central tendency, qualitative variation, degree of dispersion, and symmetrical uncertainty. FS is optimized using the self-improved Aquila optimization approach. Finally, a hybridized model combining long short-term memory and a quantum neural network is trained using the selected features. An algorithm is devised to optimize the LSTM model’s weights. Performance evaluation of the proposed approach is conducted against existing models using specific performance measures.
Results
Far dataset-1, accuracy-96.69%, sensitivity-96.62%, specifity-96.77%, precision-96.03%, recall-97.86%, F1-score-96.84%, MCC-96.37%, NPV-96.25%, FPR-3.2%, FNR-3.37% and for dataset-2, accuracy-95.54%, sensitivity-95.86%, specifity-94.51%, precision-96.03%, F1-score-96.94%, MCC-93.03%, NPV-94.66%, FPR-5.4%, FNR-4.1%. The findings of this study contribute to improved CVD prediction by utilizing an efficient hybrid model with an optimized feature set.
Discussion
We have proven that our method accurately predicts cardiovascular disease (CVD) with unmatched precision by conducting extensive experiments and validating our methodology on a large dataset of patient demographics and clinical factors. QNN and LSTM frameworks with Aquila feature tuning increase forecast accuracy and reveal cardiovascular risk-related physiological pathways. Our research shows how advanced computational tools may alter sickness prediction and management, contributing to the emerging field of machine learning in healthcare. Our research used a revolutionary methodology and produced significant advances in cardiovascular disease prediction.
... Recently, many studies have been conducted to evaluate the suitability of deep learning (DL), a subfield of machine learning, as a subset of AI, in the prediction of cardiovascular disease (CVD) [10,[14][15][16] . Tseng et al [10] validated a DL-based retinal biomarker (Reti-CVD) and concluded that "Reti-CVD has the potential to identify individuals with ≥ 10% 10-year CVD risk who are likely to benefit from preventative CVD interventions". ...
... Despite promising results, a high false-negative rate in individuals at high risk of CVD indicates a need for ongoing research. Oyewola et al [16] have validated classification and prediction model based on the set of DL algorithms, which have evidenced a high accuracy (98.45%) "to diagnose whether people have CVD or not and to provide awareness or diagnosis on that". However, despite presented findings, researchers have stated that future developments of the model with various DL algorithms are required. ...
Increased life expectancy and an aging population
with exponentially growing cases of age-related ocular
and systemic diseases, including multimorbidity,
represent a public health challenge followed by medicosocial challenge with an economic burden. Artificial
intelligence (AI) was implemented to overcome the
burden, demonstrating promising results. With ongoing
advancements, the study of biomarkers in the eye
powered by cutting-edge machine learning techniques
could redefine the way ocular and general health
conditions are categorized and diagnosed and lead to
more effective preventive measures, and personalized
treatments capable to provide the patients with the best
care possible.
