Cardiac Abnormality Prediction Using Multiple Machine Learning Approaches

Rana, Jahid Hasan; Farhin, Moniara; Turzo, Saif Ahamed; Roy, Sagar Chandra; Nabil, Rashidul Hasan; Rupai, Aneem-Al-Ahsan

doi:10.1007/978-3-031-17181-9_4

Cited by 1 publication

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then they checked and removed the outlier by using the Interquartile Range (IQR). Finally, they used logistic regression as the classifier and got an average accuracy of 72.18% [13]. Comlan et al utilized the CRISP-DM framework to develop the prediction model, starting with data selection and preparation.…”

Section: Comparison With Other Studiesmentioning

confidence: 99%

Logistic regression for cardiovascular diseases prediction by integrating PCA and K-means ++

Miao

2024

TNS

View full text Add to dashboard Cite

This research introduces a novel method for forecasting cardiovascular diseases using an advanced combination of K-means++ clustering, Principal Component Analysis (PCA), and Logistic Regression techniques. Given the global impact of cardiovascular diseases as a primary cause of death, this research utilizes a comprehensive dataset to tackle the prediction challenges associated with CVDs. Initially employing K-means++ for enhanced data quality, followed by PCA for dimensionality reduction, the study applies Logistic Regression for outcome prediction, achieving remarkable accuracy, specificity, and sensitivity. This methodological rigor offers a promising avenue for early and accurate CVDs detection, significantly outperforming traditional predictive models. By refining data through these steps, the study ensures the predictive model is built on a solid foundation, enhancing the reliability and generalizability of the predictions. The integration of these advanced analytical techniques marks a step forward in the pursuit of effective cardiovascular disease management, highlighting the importance of data preprocessing in predictive modeling.

show abstract

Section: Comparison With Other Studiesmentioning

confidence: 99%

Logistic regression for cardiovascular diseases prediction by integrating PCA and K-means ++

Miao

2024

TNS

View full text Add to dashboard Cite

show abstract

Cardiac Abnormality Prediction Using Multiple Machine Learning Approaches

Cited by 1 publication

References 12 publications

Logistic regression for cardiovascular diseases prediction by integrating PCA and K-means ++

Logistic regression for cardiovascular diseases prediction by integrating PCA and K-means ++

Contact Info

Product

Resources

About