Optimized stacking ensemble for early-stage diabetes mellitus prediction

Aman, Aman; Chhillar, Rajender Singh

doi:10.11591/ijece.v13i6.pp7048-7055

Cited by 1 publication

(1 citation statement)

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“…These frightening findings highlight the need to understand the complex causes of CVD. The complex nature of CVD, which is linked to risk factors like high blood pressure, insulin levels, smoking, and sedentary lifestyles, highlights the need for comprehensive prevention, early detection, and management strategies ( 2 ). Understanding these risk variables is essential for establishing targeted therapies and reducing the global effect of cardiovascular health issues as researchers study CVD ( 3 ).…”

Section: Introductionmentioning

confidence: 99%

Enhanced cardiovascular disease prediction through self-improved Aquila optimized feature selection in quantum neural network & LSTM model

Darolia,

Chhillar,

Alhussein

et al. 2024

Front. Med.

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IntroductionCardiovascular 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.MethodsThis 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.ResultsFar 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.DiscussionWe 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.

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Section: Introductionmentioning

confidence: 99%