EVALUATION OF MACHINE LEARNING ALGORITHMS FOR GASTROENTEROLOGICAL DISEASES PREDICTION
25.07.2023
International Scientific Journal "Science and Innovation". Series A. Volume 2 Issue 7
Yakhshiboyev E. Rustam, Muminov B. Bahodir, Eshmuradov E. Dilshod, Kudratillaev B. Meyerbek
Abstract. This article presents a study and analysis of various artificial intelligence (AI) algorithms for their application in predicting gastroenterological diseases. Gastroenterological diseases pose a significant healthcare challenge, and early detection and accurate prognosis of these conditions can greatly improve treatment outcomes and impact patients' quality of life. The analysis of multiple AI algorithms is conducted in this work, with the aim of developing a hardware-software complex for predicting gastroenterological diseases. Particular attention is given to the k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), Artificial Neural Networks, and Random Forest algorithms applied to gastroenterological patient data.
Various datasets from different sources and medical institutions were used for the research. The authors also discuss data preprocessing methods, such as normalization, feature selection, and outlier handling, to enhance the effectiveness of AI algorithms.
This work is a valuable study that advances the understanding of the applicability of AI algorithms in the field of gastroenterology. It may serve as a basis for further research and the development of innovative approaches to diagnosing and predicting gastroenterological diseases in modern medicine.
Keywords: artificial intelligence, algorithm, prediction, gastroenterological diseases, hardware-software complex, k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), Artificial Neural Networks, Random Forest, initiation.
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