Implementation of the K-Neighbors Algorithm to Detect Diabetes Web Based Application

Mohammad Farrel Nur Rilwanu, Faris Huwaidi, Hibar Taufikurachman

Abstract


Indonesia is the fifth country with the most diabetes sufferers in the world. This is influenced by an unhealthy lifestyle and then coupled with a lack of public awareness to check whether he has diabetes or not. The KNN (K-Nearest Neighbors) algorithm can be used to pr edict whether a person has diabetes. By using a dataset from the Pima Indian Diabetes Database, the data training process was carried out using the KNN algorithm and obtained decent accuracy results using a Jupyter notebook. From the results of the trained data set, it is then exported to be used in website development using the Python programming language. In the web application developed, the user is asked to input data on pregnancies (a person's pregnancy rate as long as he is alive), insulin levels, glucose levels, BMI, blood pressure, family history of diabetes, skin thickness, and age in the form of a slider. The input data is processed by the KNN algorithm to determine the outcome in the form of a positive or negative diabetes result based on the proximity of the new data entered with other data that has been trained.


Keywords


Dataset; Diabetes; KNN; Prediction; Web Application

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References


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DOI: https://doi.org/10.17509/seict.v3i1.42347

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