Analysis of the Application Effects of Machine Learning in Early Disease Diagnosis
Journal: Journal of Clinical Medicine Research DOI: 10.32629/jcmr.v6i1.3666
Abstract
Objective: To explore the application effectiveness of machine learning techniques in early disease diagnosis, compare different algorithms, and provide optimization references for disease classification tasks. Methods: A simulated dataset with 1,000 samples, including age, biomarker concentrations, and imaging features, was constructed. Logistic regression, random forest, and support vector machine (SVM) algorithms were employed for experiments. Model performance was evaluated using three metrics: accuracy, sensitivity, and specificity. Results: The random forest model outperformed others across all metrics (accuracy: 92.7%, sensitivity: 90.5%, specificity: 94.8%). The SVM achieved high specificity (92.9%) but slightly lower sensitivity (85.6%). Logistic regression demonstrated lower performance but was suitable for rapid diagnostic scenarios. Conclusion: Random forest is well-suited for diagnosing diseases with complex, nonlinear features, while SVM and logistic regression have utility in specific tasks. Future work may focus on integrating deep learning and multimodal data to further optimize diagnostic performance.
Keywords
machine learning, early disease diagnosis, random forest, support vector machine
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[1] Das A, Dhillon P. Application of machine learning in measurement of ageing and geriatric diseases: a systematic review[J].BMC Geriatrics, 2023, 23(1).
[2] Wu B, Moeckel G. Application of digital pathology and machine learning in the liver, kidney and lung diseases[J].Journal of pathology informatics, 2023.
[3] Ahuja T. Employability of the Machine Learning Algorithms in the Early Diagnosis of Various Diseases[J].International Journal of Research in Medical Sciences and Technology, 2022.
[4] Ur Rehman M, Driss M, Khakimov A,et al. Non-Invasive Early Diagnosis of Obstructive Lung Diseases Leveraging Machine Learning Algorithms[J].Computers, Materials & Continua, 2022, 72(3).
[5] Tran N, Kretsch C M, Lavalley C, et al. Machine learning and artificial intelligence for the diagnosis of infectious diseases in immunocompromised patients[J].Current Opinion in Infectious Diseases, 2023, 36:235-242.
[2] Wu B, Moeckel G. Application of digital pathology and machine learning in the liver, kidney and lung diseases[J].Journal of pathology informatics, 2023.
[3] Ahuja T. Employability of the Machine Learning Algorithms in the Early Diagnosis of Various Diseases[J].International Journal of Research in Medical Sciences and Technology, 2022.
[4] Ur Rehman M, Driss M, Khakimov A,et al. Non-Invasive Early Diagnosis of Obstructive Lung Diseases Leveraging Machine Learning Algorithms[J].Computers, Materials & Continua, 2022, 72(3).
[5] Tran N, Kretsch C M, Lavalley C, et al. Machine learning and artificial intelligence for the diagnosis of infectious diseases in immunocompromised patients[J].Current Opinion in Infectious Diseases, 2023, 36:235-242.
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