Research Article
Integration of Artificial Intelligence with Diabetic Data for Increasingly Personalized Medicine
@INPROCEEDINGS{10.1007/978-3-031-81573-7_7, author={Madiop Diouf and Thierno Amadou Diallo and Elhadji Ndiaye Diallo and Birahime Diouf and Ibra Dioum}, title={Integration of Artificial Intelligence with Diabetic Data for Increasingly Personalized Medicine}, proceedings={Towards new e-Infrastructure and e-Services for Developing Countries. 15th International Conference, AFRICOMM 2023, Bobo-Dioulasso, Burkina Faso, November 23--25, 2023, Proceedings, Part II}, proceedings_a={AFRICOMM PART 2}, year={2025}, month={2}, keywords={ANN RF RL KNN IA}, doi={10.1007/978-3-031-81573-7_7} }- Madiop Diouf
Thierno Amadou Diallo
Elhadji Ndiaye Diallo
Birahime Diouf
Ibra Dioum
Year: 2025
Integration of Artificial Intelligence with Diabetic Data for Increasingly Personalized Medicine
AFRICOMM PART 2
Springer
DOI: 10.1007/978-3-031-81573-7_7
Abstract
Diabetes is considered the most deadly and chronic disease that causes an increase in glucose. Polygenic disease is one in which the exocrine gland does not produce the hypoglycemic agent and according to the International Federation of Polygenic Diseases 382 million people live with polygenic disease in the world. By 2035, this number will double to 592 million. Diabetes mellitus or simply the disease can be a disease due to increased blood glucose levels. Many difficulties can arise if diabetes is not treated and not identified by the doctor. Thus, artificial intelligence (AI), which has become the new term we hear every day in recent years, generally defines the ability of a machine to act on its own and which is not explicitly programmed to reproduce actions or functions that are generally those of human beings. Today, we find it in our computing machines, social networks, transportation and in the medical sector etc. Therefore, machine learning is one of the disciplines of artificial intelligence that seeks to find a way to create computer programs that automatically improve with experience. In this work, we will focus on the use of machine learning algorithms for the prediction of diabetes, which is a dysfunction of the blood sugar regulation system, in order to reduce the risks of complications of this chronic disease on the health of the patient. To achieve this goal, we used machine learning algorithms such as Random Forest RF, Logistic Regression RL, K-nearest neighbors KNN and Neural Networks ANN and the data were extracted from Kaggle which is a web platform owned by Google that operates as a community for data scientists and developers. The performance of the classifiers was compared based on the accuracy rate.
