MACHINE LEARNING ALGORITHMS AND SYMPTOM CLUSTERING
Ключевые слова:
Machine Learning Algorithms, Symptom Classification, Medical Diagnostics, Integration Precision, Effectiveness, Potential Implications, Clinical Practice, Diagnostic Methodologies, Healthcare PracticesАннотация
The thesis titled "Machine Learning Algorithms and Symptom Classification" delves into the critical intersection of machine learning algorithms and medical diagnostics through symptom classification. Symptoms serve as vital indicators in diagnosing medical conditions, and the integration of machine learning algorithms enhances the precision of symptom classification. This thesis aims to explore the application of machine learning algorithms in symptom classification within the medical field, examining their effectiveness and potential implications for clinical practice. By analyzing the integration of machine learning algorithms into symptom classification, this study seeks to contribute to the advancement of diagnostic methodologies and healthcare practices.
Библиографические ссылки
Bishop, CM (2006). Pattern Recognition and Machine Learning. Springer.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Science & Business Media.
Kumar, A., & Wong, A. (2019). Machine Learning Algorithms for Healthcare Applications. Springer.
Liu, Y., & Xie, S. (2020). Machine Learning and Artificial Intelligence in Bioinformatics: Applications to Protein Modeling, Prediction, and Analysis. Elsevier.
Maron, O., & Moore, A. (1997). The racing algorithm: Model selection for lazy learners. Artificial Intelligence Review, 11(1-5), 193-225.
