Artificial Intelligence Methods in Osteoporosis Prediction Problem

Abstract

Many sectors of human activity have implemented various solutions based on artificial intelligence methods. These solutions help significantly in decision-making tasks, especially when analyzing a large amount of relevant data is required beforehand. This paper discusses developing a computer system to assist doctors in diagnosing osteoporosis based on densitometric exam results. The system was developed using machine learning and trained on patient data obtained from densitometric examinations. The STRATOS device was used to collect data at AltMed Medical Center in Armenia. The goal of the system is to provide an accurate diagnosis of osteoporosis in patients while ensuring that the diagnosis is reliable and effective. During the system’s development, we utilized three prominent machine learning models: Decision Tree, Random Forest, and SVM (Support Vector Machines). To enhance the accuracy and robustness of the system, these models were selected based on their effectiveness in solving complex classification problems. The developed system is equipped with advanced tools to detect potential diseases by exploring unidentified patterns and correlations among syndromes. The mentioned capability improves the diagnostic capabilities of the system. Achieving the medical goal requires early detection and accurate diagnosis. The AltMed Medical Center plans to utilize this system to provide medical professionals with support for informed decisions and improved patient care. The ability of the system to analyze complex medical data and reveal hidden insights makes it a valuable asset in the field.

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