Advanced Multi-Label Classification for Predicting Diverse Diseases from Comprehensive Symptom Data Using Random Forests

Öz

This paper presents the design and evaluation of an advanced multi-label classification system that predicts multiple diseases from symptom-based input data. Utilizing a dataset comprising 92 symptoms and 282 potential diseases from 653 patient records provided by home healthcare clinics, we applied a Random Forest Classifier within a multi-output framework. To ensure robustness, we compared the Random Forest model with other machine learning algorithms, including Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), and Gradient Boosting. The Random Forest model achieved the highest accuracy score of 98%, while other models also demonstrated competitive performance. The results indicate that the proposed model can serve as a reliable support tool in clinical environments, assisting in early diagnosis and enhancing the overall quality of care. Our approach distinguishes itself by effectively tackling the inherent multi-label complexity of medical diagnosis with exceptional accuracy.

Anahtar Kelimeler

• Multi-label classification
• Random forest
• Healthcare diagnostics
• Machine learning
• Symptom-based disease prediction

Kaynakça

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