Supervised machine learning models applied to disease diagnosis and prognosis
This work analyses the diagnosis and prognosis of cancer and heart disease data using five Machine Learning (ML) algorithms. We compare the predictive ability of all the ML algorithms to breast cancer and heart disease. The important variables that causes cancer and heart disease are also studied. W...
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| Main Authors: | , , |
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| Format: | Book |
| Published: |
AIMS Press,
2019-10-01T00:00:00Z.
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| Online Access: | Connect to this object online. |
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| Summary: | This work analyses the diagnosis and prognosis of cancer and heart disease data using five Machine Learning (ML) algorithms. We compare the predictive ability of all the ML algorithms to breast cancer and heart disease. The important variables that causes cancer and heart disease are also studied. We predict the test data based on the important variables and compute the prediction accuracy using the Receiver Operating Characteristic (ROC) curve. The Random Forest (RF) and Principal Component Regression (PCR) provides the best performance in analyzing the breast cancer and heart disease data respectively. |
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| Item Description: | 10.3934/publichealth.2019.4.405 2327-8994 |