Creation of a Tumor Diagnostic Tool Using Machine Learning in Python

Zynnia Echeverria; Felix Chavez; Darling Balon; Gabriel Arellano

doi:10.1007/978-3-032-19845-7_27

Creation of a Tumor Diagnostic Tool Using Machine Learning in Python

Zynnia Echeverria
, Felix Chavez
, Darling Balon
, Gabriel Arellano

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This analysis aims to design a diagnostic tool using a predictive model for the early detection of breast tumor malignancy by applying Python programming software and Machine Learning. We employed supervised and unsupervised methods, including PCA, ISOMAP, and clustering, to perform a preliminary analysis and explore the data structure. Subsequently, supervised methods are applied to train different models, using histopathological data and pre-accuracy metrics. The results highlight that LDA offers an optimal balance between accuracy and recall, being suitable for accurate differentiation between benign and malignant tumors. In response to these findings, a graphical interface trained with LDA was developed, aimed at improving early identification of tumor malignancy. This tool promises to facilitate more accurate prognostication and therapeutic intervention for patients.

Original language	English
Title of host publication	Emerging Research in Intelligent Systems - Proceedings of the ESPE CIT 2025
Editors	Gonzalo Fernando Olmedo Cifuentes, Diego Gustavo Arcos Avilés, Hernán Vinicio Lara Padilla
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	381-393
Number of pages	13
ISBN (Print)	9783032198440
DOIs	https://doi.org/10.1007/978-3-032-19845-7_27
State	Published - 2026
Externally published	Yes
Event	20th International Multidisciplinary Congress on Science and Technology, CIT 2025 - Sangolquí, Ecuador Duration: 11 Aug 2025 → 15 Aug 2025

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1880 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	20th International Multidisciplinary Congress on Science and Technology, CIT 2025
Country/Territory	Ecuador
City	Sangolquí
Period	11/08/25 → 15/08/25

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Keywords

Cell nuclei
diagnostics
Python.

Access to Document

10.1007/978-3-032-19845-7_27

Cite this

Echeverria, Z., Chavez, F., Balon, D., & Arellano, G. (2026). Creation of a Tumor Diagnostic Tool Using Machine Learning in Python. In G. F. Olmedo Cifuentes, D. G. Arcos Avilés, & H. V. Lara Padilla (Eds.), Emerging Research in Intelligent Systems - Proceedings of the ESPE CIT 2025 (pp. 381-393). (Lecture Notes in Networks and Systems; Vol. 1880 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-032-19845-7_27

Echeverria, Zynnia ; Chavez, Felix ; Balon, Darling et al. / Creation of a Tumor Diagnostic Tool Using Machine Learning in Python. Emerging Research in Intelligent Systems - Proceedings of the ESPE CIT 2025. editor / Gonzalo Fernando Olmedo Cifuentes ; Diego Gustavo Arcos Avilés ; Hernán Vinicio Lara Padilla. Springer Science and Business Media Deutschland GmbH, 2026. pp. 381-393 (Lecture Notes in Networks and Systems).

@inproceedings{46ebb1f52b8c4a098eb06c95b023877a,

title = "Creation of a Tumor Diagnostic Tool Using Machine Learning in Python",

abstract = "This analysis aims to design a diagnostic tool using a predictive model for the early detection of breast tumor malignancy by applying Python programming software and Machine Learning. We employed supervised and unsupervised methods, including PCA, ISOMAP, and clustering, to perform a preliminary analysis and explore the data structure. Subsequently, supervised methods are applied to train different models, using histopathological data and pre-accuracy metrics. The results highlight that LDA offers an optimal balance between accuracy and recall, being suitable for accurate differentiation between benign and malignant tumors. In response to these findings, a graphical interface trained with LDA was developed, aimed at improving early identification of tumor malignancy. This tool promises to facilitate more accurate prognostication and therapeutic intervention for patients.",

keywords = "Cell nuclei, diagnostics, Python.",

author = "Zynnia Echeverria and Felix Chavez and Darling Balon and Gabriel Arellano",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.; 20th International Multidisciplinary Congress on Science and Technology, CIT 2025 ; Conference date: 11-08-2025 Through 15-08-2025",

year = "2026",

doi = "10.1007/978-3-032-19845-7\_27",

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series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "381--393",

editor = "\{Olmedo Cifuentes\}, \{Gonzalo Fernando\} and \{Arcos Avil{\'e}s\}, \{Diego Gustavo\} and \{Lara Padilla\}, \{Hern{\'a}n Vinicio\}",

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Echeverria, Z, Chavez, F, Balon, D & Arellano, G 2026, Creation of a Tumor Diagnostic Tool Using Machine Learning in Python. in GF Olmedo Cifuentes, DG Arcos Avilés & HV Lara Padilla (eds), Emerging Research in Intelligent Systems - Proceedings of the ESPE CIT 2025. Lecture Notes in Networks and Systems, vol. 1880 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 381-393, 20th International Multidisciplinary Congress on Science and Technology, CIT 2025, Sangolquí, Ecuador, 11/08/25. https://doi.org/10.1007/978-3-032-19845-7_27

Creation of a Tumor Diagnostic Tool Using Machine Learning in Python. / Echeverria, Zynnia; Chavez, Felix; Balon, Darling et al.
Emerging Research in Intelligent Systems - Proceedings of the ESPE CIT 2025. ed. / Gonzalo Fernando Olmedo Cifuentes; Diego Gustavo Arcos Avilés; Hernán Vinicio Lara Padilla. Springer Science and Business Media Deutschland GmbH, 2026. p. 381-393 (Lecture Notes in Networks and Systems; Vol. 1880 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Creation of a Tumor Diagnostic Tool Using Machine Learning in Python

AU - Echeverria, Zynnia

AU - Chavez, Felix

AU - Balon, Darling

AU - Arellano, Gabriel

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

PY - 2026

Y1 - 2026

N2 - This analysis aims to design a diagnostic tool using a predictive model for the early detection of breast tumor malignancy by applying Python programming software and Machine Learning. We employed supervised and unsupervised methods, including PCA, ISOMAP, and clustering, to perform a preliminary analysis and explore the data structure. Subsequently, supervised methods are applied to train different models, using histopathological data and pre-accuracy metrics. The results highlight that LDA offers an optimal balance between accuracy and recall, being suitable for accurate differentiation between benign and malignant tumors. In response to these findings, a graphical interface trained with LDA was developed, aimed at improving early identification of tumor malignancy. This tool promises to facilitate more accurate prognostication and therapeutic intervention for patients.

AB - This analysis aims to design a diagnostic tool using a predictive model for the early detection of breast tumor malignancy by applying Python programming software and Machine Learning. We employed supervised and unsupervised methods, including PCA, ISOMAP, and clustering, to perform a preliminary analysis and explore the data structure. Subsequently, supervised methods are applied to train different models, using histopathological data and pre-accuracy metrics. The results highlight that LDA offers an optimal balance between accuracy and recall, being suitable for accurate differentiation between benign and malignant tumors. In response to these findings, a graphical interface trained with LDA was developed, aimed at improving early identification of tumor malignancy. This tool promises to facilitate more accurate prognostication and therapeutic intervention for patients.

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KW - diagnostics

KW - Python.

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AN - SCOPUS:105040018612

SN - 9783032198440

T3 - Lecture Notes in Networks and Systems

SP - 381

EP - 393

BT - Emerging Research in Intelligent Systems - Proceedings of the ESPE CIT 2025

A2 - Olmedo Cifuentes, Gonzalo Fernando

A2 - Arcos Avilés, Diego Gustavo

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Echeverria Z, Chavez F, Balon D, Arellano G. Creation of a Tumor Diagnostic Tool Using Machine Learning in Python. In Olmedo Cifuentes GF, Arcos Avilés DG, Lara Padilla HV, editors, Emerging Research in Intelligent Systems - Proceedings of the ESPE CIT 2025. Springer Science and Business Media Deutschland GmbH. 2026. p. 381-393. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-032-19845-7_27

Creation of a Tumor Diagnostic Tool Using Machine Learning in Python

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Bibliographical note

Keywords

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