The Use of Artificial Intelligence in Predicting Chemotherapy-Induced Toxicities in Metastatic Colorectal Cancer: A Data-Driven Approach for Personalized Oncology

Machine learning models learn about general behavior from data by finding the relationships between features. Our purpose was to develop a predictive model to identify and predict which subset of colorectal cancer patients are more likely to experience chemotherapy-induced toxicity and to determine...

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Published inDiagnostics (Basel) Vol. 14; no. 18; p. 2074
Main Authors Froicu, Eliza-Maria, Oniciuc, Oriana-Maria, Afrăsânie, Vlad-Adrian, Marinca, Mihai-Vasile, Riondino, Silvia, Dumitrescu, Elena Adriana, Alexa-Stratulat, Teodora, Radu, Iulian, Miron, Lucian, Bacoanu, Gema, Poroch, Vladimir, Gafton, Bogdan
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 19.09.2024
MDPI
Subjects
Artificial intelligence
Bone marrow
Cancer
Cancer therapies
Chemotherapy
chemotherapy toxicity
Colorectal cancer
Complications and side effects
Datasets
Forecasts and trends
Growth factors
Hepatitis
Machine learning
Medical prognosis
Medical research
Medical technology
Medicine, Experimental
Metastasis
metastatic colorectal cancer
Oncology
Patients
prediction model
Toxicity
Romania
metastatic colorectal cancer
chemotherapy toxicity
prediction model
artificial intelligence
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Summary:Machine learning models learn about general behavior from data by finding the relationships between features. Our purpose was to develop a predictive model to identify and predict which subset of colorectal cancer patients are more likely to experience chemotherapy-induced toxicity and to determine the specific attributes that influence the presence of treatment-related side effects. The predictor was general toxicity, and for the construction of our data training, we selected 95 characteristics that represent the health state of 74 patients prior to their first round of chemotherapy. After the data were processed, Random Forest models were trained to offer an optimal balance between accuracy and interpretability. We constructed a machine learning predictor with an emphasis on assessing the importance of numerical and categorical variables in relation to toxicity. The incorporation of artificial intelligence in personalizing colorectal cancer management by anticipating and overseeing toxicities more effectively illustrates a pivotal shift towards more personalized and precise medical care.
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These authors contributed equally to this work.
ISSN:2075-4418
2075-4418
DOI:10.3390/diagnostics14182074