Optimizing early diagnosis by integrating multiple classifiers for predicting brain stroke and critical diseases
Ravnoor Singh, Satinder Kaur, Gurpreet Singh, Mehakdeep Kaur, Parminder Kaur · 2024
Read Optimizing early diagnosis by integrating multiple classifiers for predicting brain stroke and critical diseases by Ravnoor Singh, Satinder Kaur, Gurpreet Singh, Mehakdeep Kaur, Parminder Kaur (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11574051.txt.
Description
Ravnoor Singh and co-authors combine many machine learning classifiers to predict five serious diseases early: stroke, heart attack, cancer, Alzheimer's, and Parkinson's. Early warning can change treatment decisions across all five. The team trained individual classifiers on five datasets and then merged pairs of them with voting schemes.
For stroke, sixty-four models were built in total, eight single and the rest paired. A hard-voting pair of random forest and decision tree reached ninety-nine percent accuracy, with perfect recall of true cases. Different single models led for the other diseases, with a boosting method best for cancer and Parkinson's in the reported results. The authors argue that carefully merged classifiers beat single ones for early diagnosis support.
Catalog record
| CREATOR | Ravnoor Singh, Satinder Kaur, Gurpreet Singh, Mehakdeep Kaur, Parminder Kaur |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 49 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |