Machine learning in health condition check-up: An approach using Breiman's random forest algorithm

被引：0

作者：

Abd Algani Y.M. ^{[1
,2
]}

Ritonga M. ^{[3
]}

Kiran Bala B. ^{[4
]}

Al Ansari M.S. ^{[5
]}

Badr M. ^{[6
,7
]}

Taloba A.I. ^{[8
,9
]}

机构：

[1] Department of Mathematics, Sakhnin College

[2] Department of Mathematics, The Arab Academic College for Education in Israel-Haifa

[3] Department of Artificial Intelligence and Data Science, K.Ramakrishnan College of Engineering (Autonomous), Samayapuram, 621112, Trichy

[4] College of Engineering, Department of Chemical Engineering, University of Bahrain

[5] The University of Mashreq, Research Center, Baghdad

[6] Department of Medical Instruments Engineering Techniques, Al-Farahidi University, Baghdad

[7] Department of Computer Science, College of Science and Arts in Qurayyat, Jouf University

[8] Information System Department, Faculty of Computers and Information, Assiut University, Assiut

来源：

Measurement: Sensors | 2022年 / 23卷

关键词：

Bagging; Classifications; Health checking; Machine learning; Random forest algorithm;

D O I：

10.1016/j.measen.2022.100406

中图分类号：

学科分类号：

摘要：

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. © 2022

引用

共 25 条

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