Predicting High-Risk Prostate Cancer Using Machine Learning Methods
Prostate cancer can be low- or high-risk to the patient’s health. Current screening on the basis of prostate-specific antigen (PSA) levels has a tendency towards both false positives and false negatives, both of which have negative consequences. We obtained a dataset of 35,875 patients fro...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-09-01
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| Series: | Data |
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| Online Access: | https://www.mdpi.com/2306-5729/4/3/129 |
| _version_ | 1828152298062217216 |
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| author | Henry Barlow Shunqi Mao Matloob Khushi |
| author_facet | Henry Barlow Shunqi Mao Matloob Khushi |
| author_sort | Henry Barlow |
| collection | DOAJ |
| description | Prostate cancer can be low- or high-risk to the patient’s health. Current screening on the basis of prostate-specific antigen (PSA) levels has a tendency towards both false positives and false negatives, both of which have negative consequences. We obtained a dataset of 35,875 patients from the screening arm of the National Cancer Institute’s Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. We segmented the data into instances without prostate cancer, instances with low-risk prostate cancer, and instances with high-risk prostate cancer. We developed a pipeline to deal with imbalanced data and proposed algorithms to perform preprocessing on such datasets. We evaluated the accuracy of various machine learning algorithms in predicting high-risk prostate cancer. An accuracy of 91.5% can be achieved by the proposed pipeline, using standard scaling, SVMSMOTE sampling method, and AdaBoost for machine learning. We then evaluated the contribution of rate of change of PSA, age, BMI, and filtration by race to this model’s accuracy. We identified that including the rate of change of PSA and age in our model increased the area under the curve (AUC) of the model by 6.8%, whereas BMI and race had a minimal effect. |
| first_indexed | 2024-04-11T22:09:54Z |
| format | Article |
| id | doaj.art-4f35a9fba9db4d4381affbd94d65552d |
| institution | Directory Open Access Journal |
| issn | 2306-5729 |
| language | English |
| last_indexed | 2024-04-11T22:09:54Z |
| publishDate | 2019-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj.art-4f35a9fba9db4d4381affbd94d65552d2022-12-22T04:00:36ZengMDPI AGData2306-57292019-09-014312910.3390/data4030129data4030129Predicting High-Risk Prostate Cancer Using Machine Learning MethodsHenry Barlow0Shunqi Mao1Matloob Khushi2School of Computer Science, University of Sydney, 2006 Sydney, AustraliaSchool of Computer Science, University of Sydney, 2006 Sydney, AustraliaSchool of Computer Science, University of Sydney, 2006 Sydney, AustraliaProstate cancer can be low- or high-risk to the patient’s health. Current screening on the basis of prostate-specific antigen (PSA) levels has a tendency towards both false positives and false negatives, both of which have negative consequences. We obtained a dataset of 35,875 patients from the screening arm of the National Cancer Institute’s Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. We segmented the data into instances without prostate cancer, instances with low-risk prostate cancer, and instances with high-risk prostate cancer. We developed a pipeline to deal with imbalanced data and proposed algorithms to perform preprocessing on such datasets. We evaluated the accuracy of various machine learning algorithms in predicting high-risk prostate cancer. An accuracy of 91.5% can be achieved by the proposed pipeline, using standard scaling, SVMSMOTE sampling method, and AdaBoost for machine learning. We then evaluated the contribution of rate of change of PSA, age, BMI, and filtration by race to this model’s accuracy. We identified that including the rate of change of PSA and age in our model increased the area under the curve (AUC) of the model by 6.8%, whereas BMI and race had a minimal effect.https://www.mdpi.com/2306-5729/4/3/129prostate cancer screeningPSA rate of changemachine learningimbalanced dataset |
| spellingShingle | Henry Barlow Shunqi Mao Matloob Khushi Predicting High-Risk Prostate Cancer Using Machine Learning Methods Data prostate cancer screening PSA rate of change machine learning imbalanced dataset |
| title | Predicting High-Risk Prostate Cancer Using Machine Learning Methods |
| title_full | Predicting High-Risk Prostate Cancer Using Machine Learning Methods |
| title_fullStr | Predicting High-Risk Prostate Cancer Using Machine Learning Methods |
| title_full_unstemmed | Predicting High-Risk Prostate Cancer Using Machine Learning Methods |
| title_short | Predicting High-Risk Prostate Cancer Using Machine Learning Methods |
| title_sort | predicting high risk prostate cancer using machine learning methods |
| topic | prostate cancer screening PSA rate of change machine learning imbalanced dataset |
| url | https://www.mdpi.com/2306-5729/4/3/129 |
| work_keys_str_mv | AT henrybarlow predictinghighriskprostatecancerusingmachinelearningmethods AT shunqimao predictinghighriskprostatecancerusingmachinelearningmethods AT matloobkhushi predictinghighriskprostatecancerusingmachinelearningmethods |