Publication Date: 2023/12/01
Abstract: Diabetes is a prevalent chronic disease affecting a significant portion of the global population. Early detection and accurate prediction of diabetes can play a crucial role in managing the condition and preventing complications. Machine learning (ML) techniques have shown promising results in diabetes prediction based on patient data. In this study, we propose a user-understandable approach utilizing the Random Forest classifier algorithm for accurate and interpretable diabetes prediction. To build our prediction model, we utilized a comprehensive dataset comprising various patient attributes, including age, body mass index (BMI), blood pressure, glucose levels, and medical history. Pre-processing techniques were applied to handle missing values and normalize the data, followed by feature selection to identify the most relevant attributes for diabetes prediction. The user- understandable representation of the model facilitated effective interpretation and communication of the prediction results. This allows healthcare professionals to explain the prediction rationale to patients, promoting shared decision-making and patient engagement.
Keywords: No Keywords Available
DOI: https://doi.org/10.5281/zenodo.10245792
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT23NOV1802.pdf
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