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Prediction of Obesity Categories Based on Physical Activity Using Machine Learning Algorithms

Authors

  • Muhammad Iqbal Universitas Bina Sarana Informatika
  • Lisnawanty L Universitas Bina Sarana Informatika
  • Weiskhy Steven Dharmawan Universitas Bina Sarana Informatika
  • Rendi Septian Universitas Nusa Mandiri

DOI:

10.47709/cnahpc.v6i3.4053

Keywords:

Data Mining, Machine Learning, Obesity, Prediction, XGBoost

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Abstract

Obesity is a global health issue with rising prevalence, marked by excessive fat accumulation that poses health risks. Contributing factors include poor eating habits, lack of physical activity, and genetics, which elevate the risk of chronic diseases like type 2 diabetes, heart disease, stroke, and cancer. This study examines an obesity dataset with seven variables: Age, Gender, Height, Weight, BMI, Physical Activity Level, and Obesity Category. The analysis reveals strong correlations between Body Weight, BMI, and the Obesity Category, while Body Height shows a moderate negative correlation. Various machine learning algorithms were tested, including XGBoost, AdaBoost, Gradient Boosting, and Extra Trees Classification. XGBoost emerged as the top performer, achieving the highest accuracy (0.9961) and an almost perfect AUC (0.9992), making it highly effective for obesity prediction. The study's significance lies in its ability to elucidate the key factors contributing to obesity and their interactions. By recognizing the strong links between Body Weight, BMI, and Obesity Category, healthcare professionals can craft more targeted interventions. Furthermore, the successful application of advanced machine learning algorithms underscores the potential for technology to enhance predictive accuracy and support healthcare decision-making. The findings highlight XGBoost's superior performance, demonstrating its value in predicting obesity and aiding in early diagnosis and prevention strategies. This research emphasizes the critical role of data and technology in tackling obesity and improving public health outcomes.

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References

Admojo, F. T., & Rismayanti, N. (2024). Estimating Obesity Levels Using Decision Trees and K-Fold Cross-Validation?: A Study on Eating Habits and Physical Conditions. Indonesian Journal of Data and Science, 5(1), 37–44.

Andreyestha, & Subekti, A. (2020). Analisis Sentiment pada Ulasan Film Dengan Optimasi Ensemble Learning. JURNAL INFORMATIKA, 7(1), 5–8.

Cendani, L. M., & Wibowo, A. (2022). Perbandingan Metode Ensemble Learning pada Klasifikasi Penyakit Diabetes. Jurnal Masyarakat Informatika, 13(1), 33–44.

Elina, S., Yulianti, H., Soesanto, O., & Sukmawaty, Y. (2022). Penerapan Metode Extreme Gradient Boosting ( XGBOOST ) pada Klasifikasi Nasabah Kartu Kredit. Journal of Mathematics: Theory and Applications, 4(1), 21–26.

Fitriani, D. N., & Bahri, S. (2024). Prediksi tingkat obesitas menggunakan neural network?: pendekatan klasifikasi biner. PARAMETER JURNAL MATEMATIKA, STATISTIKA DAN TERAPANNYA, 03(01), 85–92.

Hozairi, H., Anwari, A., & Alim, S. (2021). Implementasi Orange Data Mining Untuk Klasifikasi Kelulusan Mahasiswa Dengan Model K-Nearest Neighbor, Decision Tree Serta Naive Bayes. Network Engineering Research Operation, 6(2), 133. https://doi.org/10.21107/nero.v6i2.237

Ishaq, A., Sadiq, S., Umer, M., Ullah, S., Mirjalili, S., Rupapara, V., & Nappi, M. (2021). Improving the Prediction of Heart Failure Patients’ Survival Using SMOTE and Effective Data Mining Techniques. IEEE Access, 9, 39707–39716. https://doi.org/10.1109/ACCESS.2021.3064084

Lior-sadaka, I., & Greenberg, D. (n.d.). Assessing Screening Methods and Machine Learning for Predicting Childhood Overweight and Obesity?: A Population-Based Study Background?: Methods?: Results?: Conclusions?: 1–18.

Mailo, F. F., & Lazuardi, L. (2019). Analisis Sentimen Data Twitter Menggunakan Metode Text Mining Tentang Masalah Obesitas di Indonesia. Journal of Information Systems for Public Health, 4(1).

MRSIMPLE. (2020). Obesity Prediction. https://doi.org/10.34740/kaggle/dsv/7479144

Nasser, M. S. A., & Abu-naser, S. S. (2023). Predictive Modeling of Obesity and Cardiovascular Disease Risk?: A Random Forest Approach. International Journal of Academic Information Systems Research (IJAISR), 7(12), 26–38.

Nawawi, H. M., Hikmah, A. B., Mustopa, A., & Wijaya, G. (2024). Model Klasifikasi Machine Learning untuk Prediksi Ketepatan Penempatan Karir. Jurnal Saintekom (Sains, Teknologi, Komputer Dan Manajemen), 14(1), 13–25.

Rahmawati, M., Lestari, A. F., & Hardani, S. (2024). Phyton-Based Machine Learning Algorithm to Predict Obesity Risk Factors in Adult Populations. Paradigma, 26(1), 51–57.

Septiana Rizky, P., Haiban Hirzi, R., Hidayaturrohman, U., Hamzanwadi Selong Jl TGKH Muhammad Zainuddin Abdul Madjid Pancor, U., & Timur, L. (2022). Perbandingan Metode LightGBM dan XGBoost dalam Menangani Data dengan Kelas Tidak Seimbang. In J Statistika (Vol. 15, Issue 2). www.unipasby.ac.id

Tandiono, S. M., & Sanjaya, S. A. (2023). Machine Learning Approach of Obesity Level Classification: A Systematic Literature Review of Methods and Factors. G-Tech?: Jurnal Teknologi Terapan, 8(1), 196–208.

Wildan, A., Burhansyah, H. A., & Ferdiansyah, C. (2024). Prediction of Obesity Classification Using K-Means Clustering. Journal of Dinda Data Science, Information Technology, and Data Analytics, 4(1), 14–22.

Wulandari, A., Mulya, A., & Dermawan, T. (2024). Application of Artificial Neural Network , K-Nearest Neighbor and Naive Bayes Algorithms for Classification of Obesity Risk Cardiovascular Disease. IJATIS: Indonesian Journal of Applied Technology and Innovation Science, 1(February), 9–15.

Xaverius Widiantoro, F., & Sinaga, F. (2020). A Concept Analysis: Physical Activity Level. In Malahayati International Journal of Nursing and Health Science (Vol. 03, Issue 1). https://ejurnalmalahayati.ac.id/index.php/nursing/article/view/2413/pdf

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ARTICLE Published HISTORY

Submitted Date: 2024-06-11
Accepted Date: 2024-06-11
Published Date: 2024-07-02

How to Cite

Muhammad Iqbal, L, L., Steven Dharmawan, W., & Septian, R. (2024). Prediction of Obesity Categories Based on Physical Activity Using Machine Learning Algorithms. Journal of Computer Networks, Architecture and High Performance Computing, 6(3), 1025-1034. https://doi.org/10.47709/cnahpc.v6i3.4053