{
  "abstract": "Choosing which features go into a clinical risk model is not only a statistical problem. A model that is accurate but whose feature list makes no clinical sense will not be trusted by the physicians it is meant to help, and most feature-selection methods still include a clinician’s judgment only as a filter applied after the search, not as part of it. This paper introduces KGEN, a genetic algorithm that builds an oncologist’s feature ratings directly into both the starting population and the fitness function, alongside data-driven importance scores, through one tunable weight. We tested this on 67,858 women from the PLCO Cancer Screening Trial, searching over 75 candidate features, against a matched no-expert control run under an identical search budget. Across four independent runs, expert guidance raised clinical relevance substantially and consistently (0.929–1.000 versus 0.682 for the no-expert control and 0.669–0.720 for four of the five conventional baselines, the exception being BCRAT, whose fixed feature set happens to reach a perfect 1.000; pooled p=0.0019 across sub-populations), while discrimination stayed within the same narrow range as every conventional baseline throughout, and, in three of the four sub-populations, was even modestly but significantly higher than the no-expert control’s: a clinically sensible feature list did not come at the cost of accuracy. The same wide-margin clinical-relevance pattern held on an independent hospital-readmission dataset of 99,343 encounters and an independent breast-cancer gene-expression cohort, each with a small, measurable accuracy trade-off against the strongest conventional baselines. Feature-selection stability across runs was also higher under expert guidance for three of four sub-populations. This suggests that a clinician’s judgment can be built into a search algorithm as a working input, not just a check performed afterward.",
  "abstract_shown": true,
  "affiliations": [
    "School of Mathematics and Computer Science (SMCS), Institute of Business Administration (IBA), Karachi 75270, Pakistan",
    "The Aga Khan University Hospital, Karachi 74800, Pakistan",
    "Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Saadia Humayun",
      "orcid": "0009-0001-9266-7835"
    },
    {
      "affs": [
        0
      ],
      "name": "Tariq Mahmood"
    },
    {
      "affs": [
        1
      ],
      "name": "Munira Moosajee"
    },
    {
      "affs": [
        2
      ],
      "name": "Muhammad Shoaib Siddiqui",
      "orcid": "0000-0002-5656-0416"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8h63kjdf2gj8j.bib",
  "cited_by": 0,
  "doi": "10.3390/math14193591",
  "fulltext_url": null,
  "id": "8h63kjdf2gj8j",
  "issns": [
    "2227-7390"
  ],
  "landing_url": "https://www.mdpi.com/2227-7390/14/19/3591",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3605|Artificial Intelligence in Healthcare"
    ]
  ],
  "openalex": "W7216390608",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-02",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/8h63kjdf2gj8j.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:29Z",
      "native_id": "10.3390/math14193591",
      "source": "crossref",
      "url": "https://www.mdpi.com/2227-7390/14/19/3591"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "issn:2227-7390",
      "subfield": "general"
    }
  ],
  "title": "KGEN: A Knowledge-Guided Evolutionary Framework for Feature Selection in Breast Cancer Risk Assessment",
  "title_text": "KGEN: A Knowledge-Guided Evolutionary Framework for Feature Selection in Breast Cancer Risk Assessment",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:29Z",
  "url": "https://research.draconitic.com/paper/8h63kjdf2gj8j",
  "venue": "Mathematics",
  "versions": []
}