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

• DOI: 10.1016/j.measen.2022.100406
• Publication Date: 2022-08-09T02:24:07Z
• Authors: Yousef Methkal Ab...; Mahyudin Ritonga; B. Kiran Bala; Mohammed Saleh Al...; Malek Badr; Ahmed I. Taloba
• Keywords: Random forest algorithm; Bagging; Machine learning; 0211 other engineering and technologies; Health checking; 02 engineering and technology; Classifications; Electric apparatus and materials. Electric circuits. Electric networks; TK452-454.4

Nowadays majority of the college students' physical condition is worrying. They are not physically and also mentally healthy. If so, why? Their selection foods consistent. Thus, they more likely to suffer from chronic illnesses such as diabetes, hypertension, stress, etc. in future. Awareness should be created prevent diseases before occur. Physiological parameters measured included Systolic (SBP) Diastolic (DBP) Blood Pressure, Body mass Index (BMI), Serum Cholesterol (BSC), percentage Fat (%BF). These retrieved classified check health or predict if any abnormalities found students. Therefore, classify their status using Breiman's Random Forest (RF) Algorithm proposed this paper. Of all classification methods available, random forests offer greatest accuracy. forest method handles large data with thousands variables. When a class sparse than further classes it can spontaneously balance sets. The outcome shows that algorithm accurate predicting checking Students' diagnosed through method. By knowing healthy body students, physician know whether not.