June 6, 2022Engineering Technology & Applied Science ResearchOpen Access

An Improved Auto Categorical PSO with ML for Heart Disease Prediction

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Why the study?

Cardiovascular diseases have the highest mortality rate worldwide, but accurate prognosis at an early stage may increase survival chances.

Population

Three heart disease datasets from the UCI ML library (Cleveland, Statlog, and Hungarian)

Comparison

ML combined with IACPSO optimization vs ML methods alone

Design

Machine learning prediction and optimization model evaluation

Authors

ADAnimesh Kumar DubeyMadhya Pradesh Council of Science and Technology ASA. K. SinhalJK Lakshmipat University RSRicha SharmaAmity University

Discussion

Member takes

Implication

ML heart disease prediction models need prospective validation; leaves open routine clinical use.

Structured PICO

Population

Three heart disease datasets from the UCI ML library (Cleveland, Statlog, and Hungarian) using 14 relevant attributes.

Intervention

Machine Learning algorithms (Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, SVM by Grid Search, K-Nearest Neighbor, Naive Bayes) combined with Improved Auto Categorical Particle Swarm Optimization (IACPSO) for feature selection.

Comparator

Machine Learning algorithms applied separately without IACPSO.

Outcome

Prediction performance parameters including accuracy (AC), precision (PR), sensitivity (SV), F-score (FS), and Matthews Correlation Coefficient (MCC).

Combining machine learning algorithms with the IACPSO optimization method for feature selection significantly improves the accuracy of heart disease prediction models.

Limitations

Future work needed with more parameters
Needs validation on real or primary datasets
Needs testing with various other threshold mechanisms

Cite This Study

Dubey et al. (2022) studied this question.

synapsesocial.com/papers/6a71fd5e26a7f98052de1385 https://doi.org/10.48084/etasr.4854

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June 6, 2022Engineering Technology & Applied Science ResearchOpen Access

An Improved Auto Categorical PSO with ML for Heart Disease Prediction

View Full Paper

Ask AI

Bookmark

Why the study?

Cardiovascular diseases have the highest mortality rate worldwide, but accurate prognosis at an early stage may increase survival chances.

Population

Three heart disease datasets from the UCI ML library (Cleveland, Statlog, and Hungarian)

Comparison

ML combined with IACPSO optimization vs ML methods alone

Design

Machine learning prediction and optimization model evaluation

Authors

ADAnimesh Kumar DubeyMadhya Pradesh Council of Science and Technology ASA. K. SinhalJK Lakshmipat University RSRicha SharmaAmity University

Discussion

Member takes

Implication

ML heart disease prediction models need prospective validation; leaves open routine clinical use.

Structured PICO

Population

Three heart disease datasets from the UCI ML library (Cleveland, Statlog, and Hungarian) using 14 relevant attributes.

Intervention

Comparator

Machine Learning algorithms applied separately without IACPSO.

Outcome

Prediction performance parameters including accuracy (AC), precision (PR), sensitivity (SV), F-score (FS), and Matthews Correlation Coefficient (MCC).

Combining machine learning algorithms with the IACPSO optimization method for feature selection significantly improves the accuracy of heart disease prediction models.

Limitations

Future work needed with more parameters
Needs validation on real or primary datasets
Needs testing with various other threshold mechanisms

Cite This Study

Dubey et al. (2022) studied this question.

synapsesocial.com/papers/6a71fd5e26a7f98052de1385 https://doi.org/10.48084/etasr.4854

Also Consider

Synapse has enriched 3 closely related papers on similar clinical questions. Consider them for comparative context:

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