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(Sk)2: Saadaali Salamti fi Kidney Kanceri – predicting survivability of kidney cancer using a multi – tier classification framework paper by Independent / Community
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(Sk)2: Saadaali Salamti fi Kidney Kanceri – predicting survivability of kidney cancer using a multi – tier classification framework

by Independent / Community arxiv-paper--unknown--00028bcc0a6729a7f87d27fca396433552026585
Nexus Index
38.5 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 0
R: Recency 100
Q: Quality 60
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0 DL / 30D
0.0%
High Impact 0 Citations
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Registry ID arxiv-paper--unknown--00028bcc0a6729a7f87d27fca396433552026585
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Provider semantic_scholar
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Cite this paper

Academic & Research Attribution

BibTeX 
@misc{arxiv_paper__unknown__00028bcc0a6729a7f87d27fca396433552026585,
  author = {Unknown},
  title = {(Sk)2: Saadaali Salamti fi Kidney Kanceri – predicting survivability of kidney cancer using a multi – tier classification framework Paper},
  year = {2026},
  howpublished = {\url{https://free2aitools.com/paper/arxiv-paper--unknown--00028bcc0a6729a7f87d27fca396433552026585}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
Unknown. (2026). (Sk)2: Saadaali Salamti fi Kidney Kanceri – predicting survivability of kidney cancer using a multi – tier classification framework [Paper]. Free2AITools. https://free2aitools.com/paper/arxiv-paper--unknown--00028bcc0a6729a7f87d27fca396433552026585

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Methodology Index Protocol
38.5
TOP 100% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 0
Popularity (P) 0
Recency (R) 100
Quality (Q) 60

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FNI V2.0 for (Sk)2: Saadaali Salamti fi Kidney Kanceri – predicting survivability of kidney cancer using a multi – tier classification framework: Semantic (S:50), Authority (A:0), Popularity (P:0), Recency (R:100), Quality (Q:60).

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❝ Cite Node 

@article{Unknown2026(Sk)2:,
  title={(Sk)2: Saadaali Salamti fi Kidney Kanceri – predicting survivability of kidney cancer using a multi – tier classification framework},
  author={},
  journal={arXiv preprint arXiv:arxiv-paper--unknown--00028bcc0a6729a7f87d27fca396433552026585},
  year={2026}
}

Abstract & Analysis

Kidney cancer is known as one of the deadliest cancers, with high morbidity and mortality rate. Survivability from cancers is always the patient's first concern. Thus, methods need to be devised to find the survivability rate. To achieve this goal, machine learning models are effective at providing aid to clinicians because of their accuracy. In this research paper SEER (Surveillance, Epidemiology, and End Results) database was used to provide information on cancer statistics. Three different approaches were used in this paper. The first experiment used regression models, the second included multi-class classification models to predict the survivability rate, and the third used multi-tier classification. In the end, we achieved 71% accuracy through our models to predict the survivability of a patient

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id
arxiv-paper--unknown--00028bcc0a6729a7f87d27fca396433552026585
slug
unknown--00028bcc0a6729a7f87d27fca396433552026585
source
semantic_scholar
author
Unknown
license
ArXiv
tags
paper, research, academic

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