Development of a Medical Analytical System Based on Intelligent Data Analysis

Explores the design and implementation of a medical analytical system based on intelligent data analysis methods.It discusses modern approaches and machine learning algorithms applied to disease prediction and medical data analysis.The study presents experimental results obtained using various algorithms, including Naive Bayes, Decision Tree, Random Forest, and Gradient Boosting, on medical datasets.The experiments cover data preprocessing, feature selection, and classification quality assessment.Python, Django, and PostgreSQL were chosen as the software platform for system development, ensuring both flexibility and scalability.The primary objective of the system is to provide medical professionals with accessible analytical tools without requiring in-depth knowledge of machine learning.Experimental results demonstrate that ensemble methods, particularly Random Forest and Gradient Boosting, achieved the highest effectiveness.