Application of Data Mining Using Apriori to Find Patterns of Asthma in Medical Record Data at the Health Center (Case Study: Datar City Health Center)

Halimatussadiah; Relita Buaton; Siswan Syahputra

doi:10.59934/jaiea.v5i1.1672

Authors

Halimatussadiah STMIK Kaputama
Relita Buaton STMIK KAPUTAMA
Siswan Syahputra STMIK KAPUTAMA

DOI:

https://doi.org/10.59934/jaiea.v5i1.1672

Keywords:

Apriori Algorithm, RapidMiner, Medical Records

Abstract

Medical records are a very important source of information in the world of health. Medical records document a patient's medical history, diagnosis, treatment, and care patterns at health facilities. However, with the large amount of data that continues to grow every day, it is often difficult for medical personnel and health facility managers to manually analyze and find useful patterns. Community health centers, as primary healthcare facilities, play an important role in addressing public health issues. Community health centers often face limitations in effectively processing available data. Therefore, methods are needed to help uncover hidden information from medical record data. One approach that can be used to analyze big data is data mining. Data mining allows users to find patterns, trends, or certain relationships that were previously unseen. In medical records, the application of data mining techniques can help identify disease patterns, relationships between diseases, and risk factors that contribute to certain diseases by using the apriori method to obtain better health service planning. From testing using the RapidMiner application, this study identified complaints, medical history, and causal factors. The results showed that there were 5 association rules formed with the highest Best rule value of 14% support and 62% confidence. The rule was “If the causal factor is genetic, the complaint is dizziness, then the medical history includes a history of asthma since childhood.”

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