Bayesian Matrix Learning by Principle Eigenvector for Completing Missing Medical Data
Since machine learning is applied in medicine, more and more medical data for prediction has been produced by monitoring patients, such as symptoms information of diabetes. This paper establishes a frame called the Diabetes Medication Bayes Matrix (DTBM) to structure the relationship between the sym...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-03-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/5/3314 |
| _version_ | 1797615690317824000 |
|---|---|
| author | Mandi Liu Lei Zhang Qi Yue |
| author_facet | Mandi Liu Lei Zhang Qi Yue |
| author_sort | Mandi Liu |
| collection | DOAJ |
| description | Since machine learning is applied in medicine, more and more medical data for prediction has been produced by monitoring patients, such as symptoms information of diabetes. This paper establishes a frame called the Diabetes Medication Bayes Matrix (DTBM) to structure the relationship between the symptoms of diabetes and the medication regimens for machine learning. The eigenvector of the DTBM is the stable distribution of different symptoms and medication regimens. Based on the DTBM, this paper proposes a machine-learning algorithm for completing missing medical data, which provides a theoretical basis for the prediction of a Bayesian matrix with missing medical information. The experimental results show the rationality and applicability of the given algorithms. |
| first_indexed | 2024-03-11T07:30:16Z |
| format | Article |
| id | doaj.art-f14f732dc8064cfcac2ae0b5e36845c0 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T07:30:16Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-f14f732dc8064cfcac2ae0b5e36845c02023-11-17T07:22:10ZengMDPI AGApplied Sciences2076-34172023-03-01135331410.3390/app13053314Bayesian Matrix Learning by Principle Eigenvector for Completing Missing Medical DataMandi Liu0Lei Zhang1Qi Yue2Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, PA 15213, USASchool of Economics and Management, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Management, Shanghai University of Engineering Science, Shanghai 201620, ChinaSince machine learning is applied in medicine, more and more medical data for prediction has been produced by monitoring patients, such as symptoms information of diabetes. This paper establishes a frame called the Diabetes Medication Bayes Matrix (DTBM) to structure the relationship between the symptoms of diabetes and the medication regimens for machine learning. The eigenvector of the DTBM is the stable distribution of different symptoms and medication regimens. Based on the DTBM, this paper proposes a machine-learning algorithm for completing missing medical data, which provides a theoretical basis for the prediction of a Bayesian matrix with missing medical information. The experimental results show the rationality and applicability of the given algorithms.https://www.mdpi.com/2076-3417/13/5/3314medical datamachine learningbayesian matrixeigenvector |
| spellingShingle | Mandi Liu Lei Zhang Qi Yue Bayesian Matrix Learning by Principle Eigenvector for Completing Missing Medical Data Applied Sciences medical data machine learning bayesian matrix eigenvector |
| title | Bayesian Matrix Learning by Principle Eigenvector for Completing Missing Medical Data |
| title_full | Bayesian Matrix Learning by Principle Eigenvector for Completing Missing Medical Data |
| title_fullStr | Bayesian Matrix Learning by Principle Eigenvector for Completing Missing Medical Data |
| title_full_unstemmed | Bayesian Matrix Learning by Principle Eigenvector for Completing Missing Medical Data |
| title_short | Bayesian Matrix Learning by Principle Eigenvector for Completing Missing Medical Data |
| title_sort | bayesian matrix learning by principle eigenvector for completing missing medical data |
| topic | medical data machine learning bayesian matrix eigenvector |
| url | https://www.mdpi.com/2076-3417/13/5/3314 |
| work_keys_str_mv | AT mandiliu bayesianmatrixlearningbyprincipleeigenvectorforcompletingmissingmedicaldata AT leizhang bayesianmatrixlearningbyprincipleeigenvectorforcompletingmissingmedicaldata AT qiyue bayesianmatrixlearningbyprincipleeigenvectorforcompletingmissingmedicaldata |