Machine learning in health condition check-up: An approach using Breiman’s random forest algorithm
Nowadays majority of the college students' physical condition is worrying. They are not physically and also mentally healthy. If so, why? Their selection of foods is not consistent. Thus, they are more likely to suffer from chronic illnesses such as diabetes, hypertension, stress, etc. in the f...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-10-01
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| Series: | Measurement: Sensors |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S266591742200040X |
| _version_ | 1828340946591285248 |
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| author | Yousef Methkal Abd Algani Mahyudin Ritonga B. Kiran Bala Mohammed Saleh Al Ansari Malek Badr Ahmed I. Taloba |
| author_facet | Yousef Methkal Abd Algani Mahyudin Ritonga B. Kiran Bala Mohammed Saleh Al Ansari Malek Badr Ahmed I. Taloba |
| author_sort | Yousef Methkal Abd Algani |
| collection | DOAJ |
| description | Nowadays majority of the college students' physical condition is worrying. They are not physically and also mentally healthy. If so, why? Their selection of foods is not consistent. Thus, they are more likely to suffer from chronic illnesses such as diabetes, hypertension, stress, etc. in the future. Awareness should be created to prevent such diseases before they occur. Physiological parameters measured included Systolic (SBP) and Diastolic (DBP) Blood Pressure, Body mass Index (BMI), Blood Serum Cholesterol (BSC), and percentage of Body Fat (%BF). These parameters are retrieved and classified to check the physical health or predict if any abnormalities are found in the health condition of college students. Therefore, to predict and classify their health status using Breiman's Random Forest (RF) Algorithm is proposed in this paper. Of all the classification methods available, random forests offer the greatest accuracy. Random forest method also handles large data with thousands of variables. When a class is more sparse than further classes in the data it can spontaneously balance the data sets. The outcome shows that the proposed Random Forest algorithm is accurate in predicting and checking the health condition of students. Students' physical condition should be diagnosed through this method. By knowing the healthy body parameters of the students, a physician can know whether they are healthy or not. |
| first_indexed | 2024-04-13T23:05:26Z |
| format | Article |
| id | doaj.art-996e815aa10c46209562e48a783e1601 |
| institution | Directory Open Access Journal |
| issn | 2665-9174 |
| language | English |
| last_indexed | 2024-04-13T23:05:26Z |
| publishDate | 2022-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Measurement: Sensors |
| spelling | doaj.art-996e815aa10c46209562e48a783e16012022-12-22T02:25:42ZengElsevierMeasurement: Sensors2665-91742022-10-0123100406Machine learning in health condition check-up: An approach using Breiman’s random forest algorithmYousef Methkal Abd Algani0Mahyudin Ritonga1B. Kiran Bala2Mohammed Saleh Al Ansari3Malek Badr4Ahmed I. Taloba5Department of Mathematics, Sakhnin College, Israel; Department of Mathematics, The Arab Academic College for Education in Israel-Haifa, Israel; Corresponding author. The Arab Academic College for Education in Israel, Israel.Muhammadiyah University of West Sumatra, IndonesiaDepartment of Artificial Intelligence and Data Science, K.Ramakrishnan College of Engineering (Autonomous), Samayapuram, 621112, Trichy, IndiaCollege of Engineering, Department of Chemical Engineering, University of Bahrain, BahrainThe University of Mashreq, Research Center, Baghdad, Iraq; Department of Medical Instruments Engineering Techniques, Al-Farahidi University, Baghdad, 10021, IraqDepartment of Computer Science, College of Science and Arts in Qurayyat, Jouf University, Saudi Arabia; Information System Department, Faculty of Computers and Information, Assiut University, Assiut, EgyptNowadays majority of the college students' physical condition is worrying. They are not physically and also mentally healthy. If so, why? Their selection of foods is not consistent. Thus, they are more likely to suffer from chronic illnesses such as diabetes, hypertension, stress, etc. in the future. Awareness should be created to prevent such diseases before they occur. Physiological parameters measured included Systolic (SBP) and Diastolic (DBP) Blood Pressure, Body mass Index (BMI), Blood Serum Cholesterol (BSC), and percentage of Body Fat (%BF). These parameters are retrieved and classified to check the physical health or predict if any abnormalities are found in the health condition of college students. Therefore, to predict and classify their health status using Breiman's Random Forest (RF) Algorithm is proposed in this paper. Of all the classification methods available, random forests offer the greatest accuracy. Random forest method also handles large data with thousands of variables. When a class is more sparse than further classes in the data it can spontaneously balance the data sets. The outcome shows that the proposed Random Forest algorithm is accurate in predicting and checking the health condition of students. Students' physical condition should be diagnosed through this method. By knowing the healthy body parameters of the students, a physician can know whether they are healthy or not.http://www.sciencedirect.com/science/article/pii/S266591742200040XRandom forest algorithmBaggingClassificationsMachine learningHealth checking |
| spellingShingle | Yousef Methkal Abd Algani Mahyudin Ritonga B. Kiran Bala Mohammed Saleh Al Ansari Malek Badr Ahmed I. Taloba Machine learning in health condition check-up: An approach using Breiman’s random forest algorithm Measurement: Sensors Random forest algorithm Bagging Classifications Machine learning Health checking |
| title | Machine learning in health condition check-up: An approach using Breiman’s random forest algorithm |
| title_full | Machine learning in health condition check-up: An approach using Breiman’s random forest algorithm |
| title_fullStr | Machine learning in health condition check-up: An approach using Breiman’s random forest algorithm |
| title_full_unstemmed | Machine learning in health condition check-up: An approach using Breiman’s random forest algorithm |
| title_short | Machine learning in health condition check-up: An approach using Breiman’s random forest algorithm |
| title_sort | machine learning in health condition check up an approach using breiman s random forest algorithm |
| topic | Random forest algorithm Bagging Classifications Machine learning Health checking |
| url | http://www.sciencedirect.com/science/article/pii/S266591742200040X |
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