[ Modèle ensembliste pour la prédiction des maladies cardiaques dans des milieux insécurisés: Cas de la Province du Nord-Kivu, RD Congo ]
Volume 39, Issue 1, March 2023, Pages 173–183
Zawadi Sirisombola Corinne1, Héritier Nsenge Mpia2, and Julien Kabuyahia3
1 Département d’Informatique de Gestion, Université de l’Assomption au Congo, B.P. 104, Butembo, Nord-Kivu, RD Congo
2 Département d’Informatique de Gestion, Université de l’Assomption au Congo, B.P 104, Butembo, Nord-Kivu, RD Congo
3 Département d’Informatique de Gestion, Université de l’Assomption au Congo, B.P. 104, Butembo, Nord-Kivu, RD Congo
Original language: French
Copyright © 2023 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Heart diseases are the leading cause of death worldwide. Nowadays, heart diseases are killing more people than ever before. Thus, the authors of this study designed this project to analyze data on heart diseases prediction. The project uses raw data in the form of a.csv file as data set. The authors collected the used dataset from the cardiology department of the Graben University Clinic (Butembo/DR Congo) that included 389 records and 25 variables including age, employment, pulse rate, blood pressure and clinical symptoms. The aim was to compare Machine Learning (ML) ensemble methods such as Boosting type (AdaBoosting, GradientBoosting and XGBoosting) with single ML models (KNN, Stochastic gradient descent (SGD), Decision Tree) to see which of the models predict better heart diseases in unstable and insecure areas. Thus, the results showed that the XGBoost model performed better with accuracy, precision and recall of 85% respectively. In this research the authors concluded that Boosting as ensemble method classifies accurately heart diseases data in an insecure area such as Butembo, in the province of North-Kivu, DR Congo.
Author Keywords: Ensemble methods, Boosting, Machine Learning, Heart disease, Insecure zone, Butembo.
Volume 39, Issue 1, March 2023, Pages 173–183
Zawadi Sirisombola Corinne1, Héritier Nsenge Mpia2, and Julien Kabuyahia3
1 Département d’Informatique de Gestion, Université de l’Assomption au Congo, B.P. 104, Butembo, Nord-Kivu, RD Congo
2 Département d’Informatique de Gestion, Université de l’Assomption au Congo, B.P 104, Butembo, Nord-Kivu, RD Congo
3 Département d’Informatique de Gestion, Université de l’Assomption au Congo, B.P. 104, Butembo, Nord-Kivu, RD Congo
Original language: French
Copyright © 2023 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Heart diseases are the leading cause of death worldwide. Nowadays, heart diseases are killing more people than ever before. Thus, the authors of this study designed this project to analyze data on heart diseases prediction. The project uses raw data in the form of a.csv file as data set. The authors collected the used dataset from the cardiology department of the Graben University Clinic (Butembo/DR Congo) that included 389 records and 25 variables including age, employment, pulse rate, blood pressure and clinical symptoms. The aim was to compare Machine Learning (ML) ensemble methods such as Boosting type (AdaBoosting, GradientBoosting and XGBoosting) with single ML models (KNN, Stochastic gradient descent (SGD), Decision Tree) to see which of the models predict better heart diseases in unstable and insecure areas. Thus, the results showed that the XGBoost model performed better with accuracy, precision and recall of 85% respectively. In this research the authors concluded that Boosting as ensemble method classifies accurately heart diseases data in an insecure area such as Butembo, in the province of North-Kivu, DR Congo.
Author Keywords: Ensemble methods, Boosting, Machine Learning, Heart disease, Insecure zone, Butembo.
Abstract: (french)
Les maladies cardiaques sont restées la principale cause de décès au niveau mondial. Cependant, elles tuent maintenant plus que jamais auparavant. C’est ainsi que les auteurs de cette étude ont conçu ce projet d’analyse des données sur la prédiction des maladies cardiaques. Le projet utilise des données brutes sous la forme d’un fichier.csv comme jeu des données. Les auteurs de cette étude ont recueilli cet ensemble de données au sein du service de cardiologie de la clinique universitaire du graben (Butembo/RD Congo) qui comprenait 389 enregistrements, après nettoyage des données, et 25 variables dont l’âge, l’emploi, le pouls, la pression artérielle et les symptômes cliniques. Le but a été de comparer les modèles ensemblistes Machine Learning de type Boosting (AdaBoosting, GradientBoosting et XGBoosting) avec ceux non ensemblistes (KNN, SGD, Arbre de décision) afin de voir lequel des modèles prédit mieux les maladies cardiaques dans des régions d’insécurités, telle que la Ville de Butembo, en RD Congo. Ainsi, les résultats ont montré que l’algorithme XGBoost a obtenu la meilleure performance de classement avec accuracy, precision et recall de 85% pour toutes ces mesures respectives. Dans cette recherche les auteurs ont montré que Boosting comme modèle d’apprentissage de type ensembliste pouvait surmonter le problème de classification d’un ensemble de données sur les maladies cardiaques dans une zone insécurisée comme Butembo.
Author Keywords: Méthodes ensemblistes, Boosting, Machine Learning, Maladies cardiaques, Zone insécurisée, Butembo.
How to Cite this Article
Zawadi Sirisombola Corinne, Héritier Nsenge Mpia, and Julien Kabuyahia, “Ensemble model for predicting heart diseases in insecure areas: The case of North-Kivu Province, DR Congo,” International Journal of Innovation and Applied Studies, vol. 39, no. 1, pp. 173–183, March 2023.
