What does this research mean for the field?

Predictive analytics using LSTM networks can effectively forecast cardiovascular disease outcomes. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The aim is to enhance cardiovascular disease forecasting using LSTM networks for better prediction accuracy.

February 21, 2026Open Access

Predictive Analytics of Cardiovascular Disease Using LSTM Networks

Q: What is the clinical evidence from this study?

Study design: Other. Population: Adults aged approximately 18-82 with demographic and physiological features related to cardiovascular disease risk (n=70000). Intervention: PCA-optimized Long Short-Term Memory (LSTM) network vs. Machine learning baseline models including Support Vector Machines, K-Nearest Neighbors, Random Forest, Gradient Boosting. Primary outcome: Diagnostic accuracy for cardiovascular disease prediction (Accuracy 96.92% vs 92% benchmark).

Key Result

PCA-optimized LSTM network achieved 96.92% accuracy and 0.9969 ROC-AUC for cardiovascular disease prediction, outperforming baseline machine learning models with 92% accuracy.

Key Points

The aim is to enhance cardiovascular disease forecasting using LSTM networks for better prediction accuracy.
Utilized LSTM (Long Short-Term Memory) networks for predictive analytics.
Analyzed data from cardiovascular profiles to train the model.
Employed machine learning techniques to optimize prediction rates.
Achieved higher accuracy in disease prediction compared to traditional methods.
Demonstrated significant improvement in early detection rates of cardiovascular issues.

Structured PICO

Does a PCA-optimized LSTM network improve the prediction of cardiovascular disease compared to traditional machine learning models in a large clinical dataset?

Population

70,000 patient records containing 12 unique features (demographic, physiological, and lifestyle data), with 27.2% marked as having cardiovascular disease.

Intervention

PCA-optimized Long Short-Term Memory (LSTM) network with class-balanced weighting

Comparator

Traditional machine learning baselines and ensemble models (e.g., Gradient Boosting)

Outcome

Predictive accuracy and ROC-AUC for cardiovascular disease detection

A PCA-optimized LSTM network with class-balanced weighting achieved 96.92% accuracy and 0.9969 ROC-AUC in predicting cardiovascular disease from tabular clinical data.

Main Result

Effect estimate: Accuracy 96.92% vs 92% benchmark

Absolute Event Rate: 96.92% vs 92%

Limitations

Study design is computational and based on retrospective dataset analysis, not a clinical trial
Lack of demographic details such as exact female proportion and age range
No randomization or blinding described
Results are performance metrics of predictive model, no clinical outcomes reported

Abstract

UGC-CARE, UGC CARE, UGC CARE list, ugc approved journal, UGC CARE, UGC CARE list, UGC CARE list of Journal, UGCCARE, care journal list, UGC-CARE list, New UGC-CARE Reference List, New ugc care journal list, Research Journal, Research Journal Publication, Research Paper, Low cost research journal, Free of cost paper publication in Research Journal, High impact factor journal, Journal, Research paper journal, UGC CARE journal, UGC CARE Journals, ugc care list of journal, ugc approved list, ugc approved list of journal, ugc approved journal,ugc care journal, ugc care approved journal,Journal publication, ISSN approved, Research journal, research paper, research paper publication, research journal publication, high impact factor, free publication, index journal, publish paper, publish Research paper, low cost publication, ugc approved journal, UGC CARE, follow norms ugc approved journal, ugc approved list of journal, ugc care journal, care journal, UGC-CARE list, New UGC-CARE Reference List, UGC CARE Journals, ugc care list of journal, ugc care list 2020, ugc care approved journal, ugc care list 2020, new ugc approved journal in 2020, IJCRT - international Research Journal, INTERNATIONAL JOURNAL OF CREATIVE RESEARCH THOUGHTS , Low cost research journal, Online international research journal, Peer-reviewed, and Refereed Journals, ISSN approved, low cost journal, Research journal, research paper, research paper publication, research journal publication, high impact factor, free publication, index journal, Regarding Approval and Equivalence of - ugc approved journal, UGC CARE, ugc approved list of journal, ugc care journal, UGC CARE list, UGCCARE, care journal, UGC-CARE list, New UGC-CARE Reference List, UGC CARE Journals, ugc care list of journal, ugc care list 2020, ugc care approved journal, ugc care list 2020, new ugc approved journal in 2020, ugc care list 2021, ugc approved journal in 2021 : ugc approved journal in 18 before care approved journal and Valid as per new UGC Gazette regulations for UGC regulations on minimum qualifications for appointment of teachers and other academic staff in universities and colleges Peer-reviewed, and Refereed Journal is valid and point in Publication. IJCRT Followed Norms, guidelines, requirement, listing criteria and methodology of score/point from: ugc approved journal, UGC CARE, ugc approved list of journal, ugc care journal, UGC CARE list, UGCCARE, care journal, UGC-CARE list, New UGC-CARE Reference List, UGC CARE Journals, ugc care list of journal, ugc care list 2020, ugc care approved journal, ugc care list 2020, new ugc approved journal in 2020, ugc care list 2021, ugc approved journal in 2021, Scopus, web of Science , scholarly journals, UGC Approved journal, ugc approved,ugc, ugc certify, publish free of cost, free publication, UGC and ISSN Approved , International Peer Reviewed, Open Access Journal , ISSN: 2320-2882 Impact Factor : 7.97 , E- journal, Low Cost INR 500, Free Publication

اسأل الذكاء الاصطناعي

Bookmark

View Full Paper

Cite This Study

Ingle et al. (Thu,) conducted a other in Adults aged approximately 18-82 with demographic and physiological features related to cardiovascular disease risk (n=70,000). PCA-optimized Long Short-Term Memory (LSTM) network vs. Machine learning baseline models including Support Vector Machines, K-Nearest Neighbors, Random Forest, Gradient Boosting was evaluated on Diagnostic accuracy for cardiovascular disease prediction (Accuracy 96.92% vs 92% benchmark). PCA-optimized LSTM network achieved 96.92% accuracy and 0.9969 ROC-AUC for cardiovascular disease prediction, outperforming baseline machine learning models with 92% accuracy.

synapsesocial.com/papers/69994ad4873532290d01f212 https://doi.org/https://doi.org/10.56975/ijcrt.v14i2.301407

اسأل الذكاء الاصطناعي

Bookmark

View Full Paper