Application of Machine Learning Algorithm for Osteoporosis Disease Prediction System
Abstract
Osteoporosis is a condition characterized by decreased bone density, leading to fragile and easily fractured bones. This disease is a significant concern as it can cause disability, fractures, and death, particularly in the elderly population. Early detection of osteoporosis is crucial to prevent disease progression through timely interventions. This study aims to develop a machine learning-based prediction system capable of detecting osteoporosis using three different algorithms, Random Forest, Support Vector Machine (SVM), and Gradient Boosting. The study involves analyzing and comparing the performance of these algorithms based on evaluation metrics such as Accuracy, Precision, Recall, and F1 Score. The data used is processed in two formats, namely ordinal and one-hot encoding, to assess the impact of encoding techniques on model performance. The results show that the Gradient Boosting algorithm performs the best on both types of data, with the highest Accuracy of 91.07% on the one-hot encoded data. Meanwhile, SVM and Random Forest also demonstrate competitive performance but with slightly lower results. This study concludes that Gradient Boosting is the most effective algorithm for osteoporosis prediction in this research. These findings can serve as a foundation for further development in the early detection of osteoporosis and support more effective and efficient prevention and treatment efforts.
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References
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