Feature-Limited Prediction on the UCI Heart Disease Dataset

Q: What does this research mean for the field?

A hybrid machine learning model utilizing feature selection can predict heart disease with 84.24% accuracy and 89.22% recall using only a limited subset of clinical features. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Khadijah Mohammad Alfadli; Alaa Omran Almagrabi

doi:10.32604/cmc.2023.033603

December 29, 2022Computers, materials & continua/Computers, materials & continua (Print)Open Access

Feature-Limited Prediction on the UCI Heart Disease Dataset

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Structured PICO

P

Population

UCI Heart Disease Dataset (patients with heart illnesses and healthy individuals)

I

Intervention

Hybrid machine learning model for feature selection and prediction

O

Outcome

Model performance (Accuracy, Recall, Precision)

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Implication

A hybrid machine learning model using feature selection on the UCI Heart Disease Dataset achieved 84.24% accuracy in discriminating patients with heart disease from healthy individuals.

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Alfadli et al. (2022) studied this question.

synapsesocial.com/papers/6a1bcf2e0a1f7575939cf6f0 https://doi.org/10.32604/cmc.2023.033603

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Feature-Limited Prediction on the UCI Heart Disease Dataset

Structured PICO

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Implication

Abstract

Discussion

Cite This Study

Feature-Limited Prediction on the UCI Heart Disease Dataset

Structured PICO

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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