SLB - SMOTE logistic blending hybrid machine learning model for chronic polycystic ovary syndrome prediction with correlated feature selection

These results establish Logistic Regression and the Blending algorithm as optimal choices for accurate and reliable PCOS prediction, demonstrating strong discriminative power and the ability to correctly classify PCOS cases.

Authors

Institutions

Publication Details

Journal
Informatics for Health and Social Care
Published
2024-10-01
DOI
https://doi.org/10.1080/17538157.2024.2405868
Citations
3
Primary Topic
Ovarian function and disorders
Type
article
Field-Weighted Citation Impact
1.61
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

SLB - SMOTE logistic blending hybrid machine learning model for chronic polycystic ovary syndrome prediction with correlated feature selection

S. Vairachilai, S. P. Raja, Anjana Tirkey, Devarakonda Anuhya
3 citations
Informatics for Health and Social Care
Ovarian function and disorders
1.61
article

SLB - SMOTE logistic blending hybrid machine learning model for chronic polycystic ovary syndrome prediction with correlated feature selection

S. Vairachilai, S. P. Raja, Anjana Tirkey, Devarakonda Anuhya
article en
3 citations

Abstract

These results establish Logistic Regression and the Blending algorithm as optimal choices for accurate and reliable PCOS prediction, demonstrating strong discriminative power and the ability to correctly classify PCOS cases.

Informatics for Health and Social CareVol. 49(3-4)
The Sanskrit College and University (IN), Barkatullah University (IN), Vellore Institute of Technology University (IN)
Zero hunger
Openalex Percentile: Top 15%
Ovarian function and disorders
1.61
DOISourceOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.