Analysis of healthy housing with Artificial Intelligence in the periurban area of Jayllihuaya Puno – 2023
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Abstract
The current trend in the construction of housing in the peri-urban area of Jayllihuaya is growing, it was observed that the terrain in the area is stable, devoid of humidity and with little presence of electromagnetic emission antennas. The study sought to establish a relationship between artificial intelligence and homes considered healthy, using supervised classification models. The analyzed characteristics of the homes were related to access to basic services, lighting, noise pollution, predominant material of the home, fuel used in cooking and access to technology. The information about the homes was collected through surveys with closed questions, subsequently processed with the purpose of predicting using classification algorithm models. The results showed that the support vector machines (SVM) algorithm model managed to correctly classify the homes with an accuracy of 82%, the k-nearest neighbors (KNN) algorithm models and the random forests (RF) achieved a accuracy of 82% and 86.3% respectively, with the particularity that the last two failed to classify the homes considered healthy. These precisions achieved show that to classify homes as healthy and unhealthy it is better to use models based on SVM.
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References
Organización Mundial de la Salud (OMS), “Informe mundial de la OMS sobre el envejecimiento y la salud.,” 2015.
P. Baker, M., Keall, M., Au, E. L., & Howden-Chapman, “Home is where the heart is--most of the time.,” New Zeal. Med. journal, 120(1264), U2769., 2007.
Organización Internacional del Trabajo (OIT), “Perspectivas sociales y del empleo en el mundo: tendencias 2016. Ginebra,” 2016.
L. F. Herrera Quintero, “Viviendas inteligentes ( Domótica ),” Rev. Ing. e Investig., vol. 25, no. 2, 2005. https://doi.org/10.15446/ing.investig.v25n2.14639
A. Gabriel, “Qué es Domótica - CEDOM | Asociación Española de Domótica e Inmótica,” CEDOM. 2020.
J. Chaparro Mendivelso, “Domótica: la mutación de la vivienda,” Scripta Nova: Revista electrónica de geografía y ciencias sociales. 2003.
W. (DC) Organización Panamericana de la Salud, “Directrices de la OMS sobre vivienda y salud [Internet],” Internet, 2022. https://www.ncbi.nlm.nih.gov/books/NBK583405/ (accessed Dec. 13, 2023).
S. Iddi, K. Muindi, H. Gitau, and B. Mberu, “Characterization of Healthy Housing in Africa: Method, Profiles, and Determinants,” J. Urban Heal., vol. 99, no. 1, 2022, https://doi.org/10.1007/s11524-021-00603-5
INEI 2018, “Dirección Nacional de Censos y Encuestas - ENDES,” 2022. https://proyectos.inei.gob.pe/endes/queesendes.asp (accessed Mar. 01, 2024).
J. E. (Hans. Korteling, G. C. van de Boer-Visschedijk, R. A. M. Blankendaal, R. C. Boonekamp, and A. R. Eikelboom, “Human- versus Artificial Intelligence,” Front. Artif. Intell., vol. 4, 2021, https://doi.org/10.3389/frai.2021.622364
L. K. Ramasamy, S. Kadry, Y. Nam, and M. N. Meqdad, “Performance analysis of sentiments in Twitter dataset using SVM models,” Int. J. Electr. Comput. Eng., vol. 11, no. 3, 2021, https://doi.org/10.11591/ijece.v11i3.pp2275-2284
S. Zhang, X. Li, M. Zong, X. Zhu, and D. Cheng, “Learning k for kNN Classification,” ACM Trans. Intell. Syst. Technol., vol. 8, no. 3, 2017, https://doi.org/10.1145/2990508
J. J. Espinosa Zúñiga, “Aplicación de algoritmos Random Forest y XGBoost en una base de solicitudes de tarjetas de crédito,” Ing. Investig. y Tecnol., vol. 21, no. 3, pp. 1–16, Jul. 2020, https://doi.org/10.22201/fi.25940732e.2020.21.3.022
J. L. Speiser, M. E. Miller, J. Tooze, and E. Ip, “A comparison of random forest variable selection methods for classification prediction modeling,” Expert Systems with Applications, vol. 134. 2019. https://doi.org/10.1016/j.eswa.2019.05.028
C. Janiesch, P. Zschech, and K. Heinrich, “Machine learning and deep learning,” Electron. Mark., vol. 31, no. 3, 2021, https://doi.org/10.1007/s12525-021-00475-2
K. Keykhosravi, A. Hamednia, H. Rastegarfar, and E. Agrell, “Data preprocessing for machine-learning-based adaptive data center transmission,” ICT Express, vol. 8, no. 1, 2022, https://doi.org/10.1016/j.icte.2022.02.002
A. A. Akimov, D. R. Valitov, and A. I. Kubryak, “DATA PREPROCESSING FOR MACHINE LEARNING,” Научное обозрение. Технические науки (Scientific Rev. Tech. Sci., no. №2 2022, 2022, https://doi.org/10.17513/srts.1391
K. Goyal, “Data Preprocessing in Machine Learning: 7 Easy Steps To Follow,” upGrad, 2021.
K. Dhasaradhan and R. Jaichandran, “Performance analysis of machine learning algorithms in heart disease prediction,” Concurr. Eng. Res. Appl., vol. 30, no. 4, 2022, https://doi.org/10.1177/1063293X221125231
W. De Vazelhes, C. J. Carey, Y. Tang, N. Vauquier, and A. Bellet, “Metric-learn: Metric Learning Algorithms in Python,” J. Mach. Learn. Res., vol. 21, 2020.
Z. Khanam, B. N. Alwasel, H. Sirafi, and M. Rashid, “Fake News Detection Using Machine Learning Approaches,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1099, no. 1, 2021, https://doi.org/10.1088/1757-899X/1099/1/012040
S. De Gheselle et al., “Machine learning for prediction of euploidy in human embryos: in search of the best-performing model and predictive features,” Fertil. Steril., vol. 117, no. 4, 2022, https://doi.org/10.1016/j.fertnstert.2021.11.029
S. Uddin, A. Khan, M. E. Hossain, and M. A. Moni, “Comparing different supervised machine learning algorithms for disease prediction,” BMC Med. Inform. Decis. Mak., vol. 19, no. 1, 2019, doi: https://doi.org/10.1186/s12911-019-1004-8
Bharadwaj, K. B. Prakash, and G. R. Kanagachidambaresan, “Pattern Recognition and Machine Learning,” in EAI/Springer Innovations in Communication and Computing, 2021. doi: https://doi.org/10.1007/978-3-030-57077-4_11
R. Santa María and P. Lima, “LA INICIATIVA DE VIVIENDA SALUDABLE EN EL PERU,” Rev. Peru. Med. Exp. y salud PÚblica, vol. 25, no. 4, 2008.
M. A. Polo and J. Sanchez, “Estudio De Mercado Para La Comercialización De Viviendas Ecológicas En El Distrito De Chiclayo - Año 2016,” 2016.
A. E. Barragán and P. E. Ochoa, Diseño de viviendas ambientales de bajo costo., vol. 5, no. 1. 2014. https://doi.org/10.18537/mskn.05.01.06