Version 1
: Received: 28 March 2023 / Approved: 28 March 2023 / Online: 28 March 2023 (14:10:14 CEST)
How to cite:
Chen, S.; Ding, Y. A Feasibility Study of Machine Learning Models for Cancer Rate Prediction. Preprints2023, 2023030491. https://doi.org/10.20944/preprints202303.0491.v1
Chen, S.; Ding, Y. A Feasibility Study of Machine Learning Models for Cancer Rate Prediction. Preprints 2023, 2023030491. https://doi.org/10.20944/preprints202303.0491.v1
Chen, S.; Ding, Y. A Feasibility Study of Machine Learning Models for Cancer Rate Prediction. Preprints2023, 2023030491. https://doi.org/10.20944/preprints202303.0491.v1
APA Style
Chen, S., & Ding, Y. (2023). A Feasibility Study of Machine Learning Models for Cancer Rate Prediction. Preprints. https://doi.org/10.20944/preprints202303.0491.v1
Chicago/Turabian Style
Chen, S. and Yuanzhao Ding. 2023 "A Feasibility Study of Machine Learning Models for Cancer Rate Prediction" Preprints. https://doi.org/10.20944/preprints202303.0491.v1
Abstract
Cancer is a major concern for people, and accurately predicting the probability of cancer incidence and mortality is an important research topic. With the development of big data and artificial intelligence technology, a new machine learning model has emerged. Using 72,591 pieces of data, including age, case count, population size, race, gender, site of onset, and year of discovery, we built a machine learning model. Through calculations, we found that the decision tree, random forest, logistic regression, support vector machine, and neural network achieved testing accuracies of 62.11%, 61.68%, 54.53%, 55.72%, and 63.10%, respectively. With the help of this model, scientists and policymakers can accurately predict future cancer incidence and mortality rates through databases, allowing them to make relevant policies (such as timely allocating doctors and medical resources) to better serve the people.
Medicine and Pharmacology, Oncology and Oncogenics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
