Application of Machine Learning Models for Survival Prognosis in Breast Cancer Studies

Iliyan Mihaylov, Maria M. Nisheva-Pavlova, Dimitar I. Vassilev

Information 2019 article cited by 56

Source Open-access PDF More in Medicine & Health

importance 6/10 ai assisted Artificial Intelligence in Healthcare oncology stage: research

Problem

Predicting breast cancer patient survival time from clinical data to support therapy and management decisions.

Why it matters

Accurate survival prognosis directly guides treatment choices for breast cancer patients.

What the AI did

Machine learning models predicted survival time using a combined tumor stage, size, and age feature, after integrating two datasets via document-oriented and graph databases.

Results

Abstract claims 'promising results' in accuracy; no concrete numbers stated

Impact

Evidence only supports promising prognostic performance on combined retrospective datasets.

Record

Venue	Information
Published	2019
Work type	article
Authors	Iliyan Mihaylov, Maria M. Nisheva-Pavlova, Dimitar I. Vassilev
Citations	56 (OpenAlex, at import time)
References	31
Model or method	not stated in source
Organization	not stated in source
Primary source	https://doi.org/10.3390/info10030093
Open access	gold
Keywords	machine learning, breast cancer, survival prognosis, data integration, clinical data

Importance rating

6 / 10

significant — thousands to millions affected

Breast cancer survival prognosis guides therapy and management decisions; abstract claims only 'promising results' with no concrete performance numbers or stated scale of impact.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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