IMPLEMENTATION OF K-NEAREST NEIGHBOR (KNN) ALGORITHM USING RAPID MINER FOR DIABETES DISEASE PREDICTION BASED ON INDIAN PIMA DATASET
Keywords:
Data Mining, K-Nearest Neighbor, RapidMiner, Diabetes Prediction, Pima Indian DatasetAbstract
The objective of this research is to use the publicly accessible Pima Indian dataset to use the K-Nearest Neighbor (KNN) algorithm for diabetes prediction. A straightforward yet powerful classification technique, the KNN method is particularly useful for processing medical data. RapidMiner software was utilized for this study's analysis method, which included data pre-processing, training and test data separation, and classification model validation. Numerous health indicators, including age, blood pressure, body mass index, and glucose levels, are included in the Pima Indian dataset and are utilized as predictive features. The test results demonstrate that the KNN algorithm can categorize patients with or without diabetes with a reasonably high degree of accuracy. Accuracy, precision, recall, and confusion matrix metrics were used to assess the model's performance. As a result, using KNN to this dataset may be a way to help the decision support system for diabetes early diagnosis.
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Copyright (c) 2025 Tetta Thirza Herdyawan, Dimas Cahyo Saputra, Gabriel Carol Aldosion, Salsha Sabilla Nurhidayat, Sukrinah (Author)
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