Classification of Asthma Diseases Using Machine Learning Models at Arun Hospital

Khalis Al Muqarrabin; Fadlisyah Fadlisyah; T Mirzal Safari

doi:10.29103/jacka.v2i2.21229

Classification of Asthma Diseases Using Machine Learning Models at Arun Hospital

Khalis Al Muqarrabin, Fadlisyah Fadlisyah, T Mirzal Safari

Abstract

Asthma is one of the chronic diseases that significantly affects the quality of life of patients. This study aims to classify asthma disease based on patient data from Arun Hospital using the K-Nearest Neighbor (KNN) algorithm. The dataset consists of 330 patient data with attributes such as allergy, itchy throat, and shortness of breath. The data went through preprocessing, transformation, and normalization stages. The KNN model was tested with a value of k = 3, resulting in three main classifications: Mild Asthma, Moderate Asthma, and Severe Asthma. The evaluation results showed a high accuracy rate, with an average of more than 90%. In addition, the model was implemented in the form of a system that visualizes the dataset, KNN analysis, and model evaluation. These findings demonstrate the potential of the KNN algorithm to provide accurate predictions and support the diagnosis of asthma disease effectively.

Keywords

Classification; K-Nearest Neighbor; Model Evaluation; Information System

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DOI: https://doi.org/10.29103/jacka.v2i2.21229