Data analysis methods for evaluating cardiovascular disease in patients
Data analysis in the healthcare sector can be an important tool for identifying patterns in the data. When it comes to low-density lipoprotein (LDL) cholesterol test results, lower values are preferable. This paper details a factor analytical method whose main objective is to create a very effective...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-02-01
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| Series: | Measurement: Sensors |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665917423000107 |
| _version_ | 1811176083663355904 |
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| author | Waleed Noori Hussein Zainab Muzahim Mohammed Zainab A. Almnaseer |
| author_facet | Waleed Noori Hussein Zainab Muzahim Mohammed Zainab A. Almnaseer |
| author_sort | Waleed Noori Hussein |
| collection | DOAJ |
| description | Data analysis in the healthcare sector can be an important tool for identifying patterns in the data. When it comes to low-density lipoprotein (LDL) cholesterol test results, lower values are preferable. This paper details a factor analytical method whose main objective is to create a very effective risk prediction model for cardiovascular incidence. Both qualitative and quantitative methods were used to generate and test the hypothesis. To investigate the attributes' associations with and relevance to cardiovascular, a data understanding analysis is specifically carried out. This paper aims to evaluate the variables affecting LDL-C by using data analytics. Consistency, convergent, and discriminant have all been considered when evaluating the prediction model. The results showed that high-density lipoprotein (HDL) significantly reduces LDL with (P 0.001, = 0.04), whereas, total cholesterol (TC) has a considerable impact on LDL with (P 0.001, r = 0.92), whereas, Very-low-density lipoprotein (VLDL) significantly affects LDL with (P 0.001, = −0.16) and, triglycerides (TG) significantly affects LDL with (P 0.001, = −0.20). The outcome will be a prediction model based on a neural network and data analysis. |
| first_indexed | 2024-04-10T19:47:23Z |
| format | Article |
| id | doaj.art-6bda6f669ce54225a7aecfb0d41386f7 |
| institution | Directory Open Access Journal |
| issn | 2665-9174 |
| language | English |
| last_indexed | 2024-04-10T19:47:23Z |
| publishDate | 2023-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Measurement: Sensors |
| spelling | doaj.art-6bda6f669ce54225a7aecfb0d41386f72023-01-29T04:22:08ZengElsevierMeasurement: Sensors2665-91742023-02-0125100674Data analysis methods for evaluating cardiovascular disease in patientsWaleed Noori Hussein0Zainab Muzahim Mohammed1Zainab A. Almnaseer2Physiology Department, AL-Zahraa College of Medicine, University of Basrah, Basrah, Iraq; Corresponding author.Biochemistry Department, AL-Zahraa College of Medicine, University of Basrah, Basrah, IraqBiochemistry Department, AL-Zahraa College of Medicine, University of Basrah, Basrah, IraqData analysis in the healthcare sector can be an important tool for identifying patterns in the data. When it comes to low-density lipoprotein (LDL) cholesterol test results, lower values are preferable. This paper details a factor analytical method whose main objective is to create a very effective risk prediction model for cardiovascular incidence. Both qualitative and quantitative methods were used to generate and test the hypothesis. To investigate the attributes' associations with and relevance to cardiovascular, a data understanding analysis is specifically carried out. This paper aims to evaluate the variables affecting LDL-C by using data analytics. Consistency, convergent, and discriminant have all been considered when evaluating the prediction model. The results showed that high-density lipoprotein (HDL) significantly reduces LDL with (P 0.001, = 0.04), whereas, total cholesterol (TC) has a considerable impact on LDL with (P 0.001, r = 0.92), whereas, Very-low-density lipoprotein (VLDL) significantly affects LDL with (P 0.001, = −0.16) and, triglycerides (TG) significantly affects LDL with (P 0.001, = −0.20). The outcome will be a prediction model based on a neural network and data analysis.http://www.sciencedirect.com/science/article/pii/S2665917423000107SensorsData analysisFactorsBig dataLDL-C |
| spellingShingle | Waleed Noori Hussein Zainab Muzahim Mohammed Zainab A. Almnaseer Data analysis methods for evaluating cardiovascular disease in patients Measurement: Sensors Sensors Data analysis Factors Big data LDL-C |
| title | Data analysis methods for evaluating cardiovascular disease in patients |
| title_full | Data analysis methods for evaluating cardiovascular disease in patients |
| title_fullStr | Data analysis methods for evaluating cardiovascular disease in patients |
| title_full_unstemmed | Data analysis methods for evaluating cardiovascular disease in patients |
| title_short | Data analysis methods for evaluating cardiovascular disease in patients |
| title_sort | data analysis methods for evaluating cardiovascular disease in patients |
| topic | Sensors Data analysis Factors Big data LDL-C |
| url | http://www.sciencedirect.com/science/article/pii/S2665917423000107 |
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