<![CDATA[Global health is currently facing serious challenges due to the increasing number of chronic disease patients, such as those with heart failure, diabetes, and cancer. This issue arises from the limitations of electronic health record (EHR) systems, which are not yet fully capable of ensuring accurate clinical diagnoses because of potential data input errors and delays in symptom identification by medical personnel. In response to this issue, this paper focuses on the integration of medical tabular data with a classification approach based on classical machine learning (ML) and large language models (LLM) to improve the accuracy of patient diagnosis predictions. This paper aims to develop and compare the performance of various ML models, such as XGBoost, SVM, and logistic regression, as well as LLM models like Gemini, LLaMA, and Qwen in fine-tuning, few-shot, and zero-shot scenarios. The paper results show that the combination of Gemini and the few-shot approach (250 shots) achieved the highest accuracy of up to 99.8% in predicting heart failure risk. The main finding of this study is that the narrative text representation of tabular data processed with LLM significantly enhances contextual understanding and classification accuracy, making this approach highly potent for application in AI-based clinical decision-making.]]>
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