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Journal : Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control

Rule-based Disease Classification using Text Mining on Symptoms Extraction from Electronic Medical Records in Indonesian Alfonsus Haryo Sangaji; Yuri Pamungkas; Supeno Mardi Susiki Nugroho; Adhi Dharma Wibawa
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 1, February 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v7i1.1377

Abstract

Recently, electronic medical record (EMR) has become the source of many insights for clinicians and hospital management. EMR stores much important information and new knowledge regarding many aspects for hospital and clinician competitive advantage. It is valuable not only for mining data patterns saved in it regarding the patient symptoms, medication, and treatment, but also it is the box deposit of many new strategies and future trends in the medical world. However, EMR remains a challenge for many clinicians because of its unstructured form. Information extraction helps in finding valuable information in unstructured data. In this paper, information on disease symptoms in the form of text data is the focus of this study. Only the highest prevalence rate of diseases in Indonesia, such as tuberculosis, malignant neoplasm, diabetes mellitus, hypertensive, and renal failure, are analyzed. Pre-processing techniques such as data cleansing and correction play a significant role in obtaining the features. Since the amount of data is imbalanced, SMOTE technique is implemented to overcome this condition. The process of extracting symptoms from EMR data uses a rule-based algorithm. Two algorithms were implemented to classify the disease based on the features, namely SVM and Random Forest. The result showed that the rule-based symptoms extraction works well in extracting valuable information from the unstructured EMR. The classification performance on all algorithms with accuracy in SVM 78% and RF 89%.
Electronic Medical Record Data Analysis and Prediction of Stroke Disease Using Explainable Artificial Intelligence (XAI) Yuri Pamungkas; Adhi Dharma Wibawa; Meiliana Dwi Cahya
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 4, November 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v7i4.1535

Abstract

The deficiency of oxygen in the brain will cause the cells to die, and the body parts controlled by the brain cells will become dysfunctional. Damage or rupture of blood vessels in the brain is better known as a stroke. Many factors affect stroke. These factors certainly need to be observed and alerted to prevent the high number of stroke sufferers. Therefore, this study aims to analyze the variables that influence stroke in medical records using statistical analysis (correlation) and stroke prediction using the XAI algorithm. Factors analyzed included gender, age, hypertension, heart disease, marital status, residence type, occupation, glucose level, BMI, and smoking. Based on the study results, we found that women have a higher risk of stroke than men, and even people who do not have hypertension and heart disease (hypertension and heart disease are not detected early) still have a high risk of stroke. Married people also have a higher risk of stroke than unmarried people. In addition, bad habits such as smoking, working with very intense thoughts and activities, and the type of living environment that is not conducive can also trigger a stroke. Increasing age, BMI, and glucose levels certainly affect a person's stroke risk. We have also succeeded in predicting stroke using the EMR data with high accuracy, sensitivity, and precision. Based on the performance matrix, PNN has the highest accuracy, sensitivity, and F-measure levels of 95%, 100%, and 97% compared to other algorithms, such as RF, NB, SVM, and KNN.