scholarly journals Peningkatan Akurasi Klasifikasi Algoritma C 4.5 Menggunakan Teknik Bagging pada Diagnosis Penyakit Jantung

2020 ◽  
Vol 7 (5) ◽  
pp. 1035 ◽  
Author(s):  
Erwin Prasetyo ◽  
Budi Prasetiyo
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Heart Disease ◽  
Medical Record ◽  
Medical Field ◽  
Medical Record Data ◽  
Patient Medical Record ◽  
C4.5 Algorithm ◽  
Record Data ◽  
Increase In Accuracy

<p class="Abstrak">Perkembangan teknologi yang begitu pesat menjadikan kebutuhan akan suatu informasi semakin meningkat, sehingga keakuratan suatu informasi menjadi suatu hal yang sangat penting, Terutama keakuratan informasi yang dibutuhkan dalam memprediksi penyakit dalam bidang medis. Dalam proses pengumpulan suatu informasi dibutuhkan metode tertentu, sehingga informasi yang telah diproses menjadi sebuah pengetahuan menggunakan suatu metode tertentu disebut dengan penambangan data atau istilah lainnya adalah <em>data mining</em>. Umumnya <em>data mining</em> digunakan untuk memprediksi suatu penyakit yang bersumber dari data rekam medis pasien, khususnya penyakit jantung. Data penyakit jantung diambil dari <em>dataset</em> <em>UCI Machine Learning Repository</em>. Tujuan dari penulis melakukan penelitian ini yaitu untuk mengetahui penerapan teknik <em>bagging</em> pada algoritma C4.5, mengetahui hasil akurasi dalam algoritma C4.5, dan membandingkan tingkat akurasi dari penerapan teknik <em>bagging</em> pada algoritma C4.5. <em>Dataset</em> yang diklasifikasikan dengan algoritma C4.5 memperoleh akurasi sebesar 72,98%. Hasil akurasi ini dapat ditingkatkan dengan menerapkan teknik bagging menghasilkan akurasi sebesar 81,84%, sehingga terjadi peningkatan akurasi sebesar 8,86%  dari penerapan teknik <em>bagging</em> pada Algoritma C4.5.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Judul2"><em>The </em><em>quick</em><em> development of technology makes the need for information increase, so that the accuracy of the information becomes a very important thing, especially the accuracy of the information needed in predicting diseases in the medical field. In the process of gathering information certain methods are needed, so information that has been processed into knowledge using a certain method is called data mining or other terms is data mining. Data mining is generally used to predict a disease originating from patient medical record data, especially heart disease. Heart disease data is taken from the UCI Machine Learning Repository dataset. The purpose of the authors conducting this research is to determine the application of bagging techniques on the C4.5 algorithm, determine the accuracy of the results in the C4.5 algorithm, and compare the level of accuracy of the application of bagging techniques on the C4.5 algorithm. The dataset classified by the C4.5 algorithm obtained an accuracy of 72.98%. The results of this accuracy can be improved by applying bagging techniques resulting in an accuracy of 81.84%, resulting in an increase in accuracy of 8.86% from the application of bagging techniques in the C4.5 Algorithm.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

scholarly journals Performance analysis of AES-Blowfish hybrid algorithm for security of patient medical record data

2018 ◽  
Vol 1007 ◽  
pp. 012018 ◽  
Author(s):  
Amir Mahmud H ◽  
Bayu Angga W ◽  
Tommy ◽  
Andi Marwan E ◽  
Rosyidah Siregar
Keyword(s):  
Performance Analysis ◽  
Medical Record ◽  
Hybrid Algorithm ◽  
Medical Record Data ◽  
Patient Medical Record ◽  
Record Data

scholarly journals Klasterisasi Data Rekam Medis Pasien Pengguna Layanan BPJS Kesehatan Menggunakan Metode K-Means

2020 ◽  
Author(s):  
Jeri Wandana ◽  
Sarjon Defit ◽  
S Sumijan
Keyword(s):  
Medical Record ◽  
Clustering Algorithm ◽  
Disease Spread ◽  
Medical Record Data ◽  
Patient Medical Record ◽  
Data Mining Approach ◽  
New Knowledge ◽  
Record Data ◽  
Class 1 ◽  
Cluster 2

Patient histories who use the services of Badan Penyelenggara Jaminan Sosial (BPJS) Kesehatan are stored in medical record data. Each medical record data contains important information that is very valuable and can be processed to explore new knowledge using a data mining approach. This study aims to help Prof. Dr. Tabrani hospital in classifying patient data who use BPJS Kesehatan, so that the pattern of disease spread is known based on class of service. The data used is patient medical record data in 2019 from October to December, the data will be processed using the K-Means Clustering algorithm with a total of 3 clusters. In cluster 0 (H0) there are 3 patients who are dominated by A09.9 disease (Diarrhea / Dysentery) in Class 2 and Class 3, for cluster 1 (H1) there are 5 patients with more diverse types of disease, while for cluster 2 (H2) there are 5 patients who are predominantly K30 disease (Dyspepsia) in Class 1.

scholarly journals Research on diabetes prediction method based on electronic medical record data analysis

2020 ◽  
Vol 185 ◽  
pp. 03001
Author(s):  
Chen Hui ◽  
Wang Mingyuan ◽  
Tang Dingjun ◽  
Zhang Longwei ◽  
Guo Ziyan ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Risk Factors ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Network Model ◽  
Neural Network Model ◽  
Medical Record Data ◽  
Electronic Medical Record Data ◽  
Record Data

The continuous progress of computer science and technology has accelerated the pace of informatization construction of the medical system. Medical technology has developed rapidly in various research directions, and the construction of medical IT systems has been continuously improved. The popular application of electronic medical records has produced massive medical data in the medical process. At the same time, in medical behavior, more and more rely on data to make relevant judgments. The coverage of medical equipment is becoming more and more extensive, and the accuracy of data is constantly improving, and the clinical diagnosis is gradually shifting from qualitative judgment to quantitative analysis. Based on the analysis of electronic medical record data, this article studies and analyzes the risk factors leading to diabetes. By analyzing the characteristic variables, the risk factors significantly related to diabetes are obtained as the input variables of the BP neural network model. For complex problems, machine learning algorithms have higher accuracy and stronger generalization capabilities. Based on the BP artificial neural network model, this paper builds and builds a machine learning simulation to predict diabetes.

scholarly journals Accuracy Implementation of Medical Record Management Information System with Waterfall Design System and ISO 9126

2020 ◽  
pp. 36-45
Author(s):  
Wahyu Wijaya Widiyanto ◽  
Sri Wulandari
Keyword(s):  
Information System ◽  
Medical Record ◽  
Management Information System ◽  
Record Management ◽  
Management Information ◽  
Medical Record Data ◽  
Patient Medical Record ◽  
Average Value ◽  
Record Data ◽  
Iso 9126

Aims: Based on the observations of researchers, some health facilities still use manual processes / have not been documented by the information system resulting in slow service, this study aims to improve health services with a medical record information system. Methodology: The method used in this study is an analysis of information systems with the waterfall method and accuracy testing with ISO 9126. Results: The results of this medical record management information system run well based on black-box testing and white box results obtained both from an average value of 82 based on the ISO 9126 scale conversion table. Conclusion: Based on the results of the average value obtained from the validation test carried out on 3 expert examiners, it can be concluded that the application for the validation system for the validation and distribution of this letter has met the ISO 9126 standard with an average good interpretation of a total value of 82, and according to be able to simplify the process of Patient Medical Record Data Management without neglecting the safety aspects of the validation and distribution process, minimizing data loss, simplifying the reporting process and facilitating the processing of patient medical record data.

Data Mining Rekam Medis Untuk Menentukan Penyakit Terbanyak Menggunakan Decision Tree C4.5

2018 ◽  
Vol 4 (1) ◽  
pp. 40-53
Author(s):  
Yuli Mardi
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Decision Tree ◽  
General Hospital ◽  
Medical Record ◽  
Analysis Data ◽  
Positive Contribution ◽  
Medical Record Data ◽  
Storage Media ◽  
Record Data

Medical record data in hospitals is rarely used for research and increase knowledge. Medical record data stored electronically or stored in the form of archives, periodically will be removed by the hospital according to existing rules, because the data is considered waste that will burden the storage media only. The main purpose of this research is how to utilize the medical record data that is considered to be a waste in order to give positive contribution for all parties both for hospital in making policy, for health facility, and for government in handling health. From data mining obtained at Citra BMC Padang General Hospital in January 2013, data analysis, data classification and decision tree making using algortima c4.5 were used, so that from total of 21 patients who got treatment got total entrophy 2,5061441 with amount most cases were found in CHAPTER XVIII (R00-R99) as many as 8 patients from 21, with sex details (female 5 patients and 3 men), age (elderly 5 patients, young and adults 1 patient, infant and child 2 patient), address (Padang Timur 4 patients, North Padang 1 patient, Lubuk Begalung 2 patient and Padang Barat 1 patient).

scholarly journals Leveraging Eye Tracking to Prioritize Relevant Medical Record Data: Comparative Machine Learning Study

10.2196/15876 ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. e15876 ◽  
Author(s):  
Andrew J King ◽  
Gregory F Cooper ◽  
Gilles Clermont ◽  
Harry Hochheiser ◽  
Milos Hauskrecht ◽  
...  
Keyword(s):  
Machine Learning ◽  
Eye Tracking ◽  
Medical Record ◽  
Information Seeking ◽  
Information Seeking Behavior ◽  
Medical Record Data ◽  
Record Data ◽  
Seeking Behavior ◽  
Manual Selection ◽  
Machine Learning Models

Background Electronic medical record (EMR) systems capture large amounts of data per patient and present that data to physicians with little prioritization. Without prioritization, physicians must mentally identify and collate relevant data, an activity that can lead to cognitive overload. To mitigate cognitive overload, a Learning EMR (LEMR) system prioritizes the display of relevant medical record data. Relevant data are those that are pertinent to a context—defined as the combination of the user, clinical task, and patient case. To determine which data are relevant in a specific context, a LEMR system uses supervised machine learning models of physician information-seeking behavior. Since obtaining information-seeking behavior data via manual annotation is slow and expensive, automatic methods for capturing such data are needed. Objective The goal of the research was to propose and evaluate eye tracking as a high-throughput method to automatically acquire physician information-seeking behavior useful for training models for a LEMR system. Methods Critical care medicine physicians reviewed intensive care unit patient cases in an EMR interface developed for the study. Participants manually identified patient data that were relevant in the context of a clinical task: preparing a patient summary to present at morning rounds. We used eye tracking to capture each physician’s gaze dwell time on each data item (eg, blood glucose measurements). Manual annotations and gaze dwell times were used to define target variables for developing supervised machine learning models of physician information-seeking behavior. We compared the performance of manual selection and gaze-derived models on an independent set of patient cases. Results A total of 68 pairs of manual selection and gaze-derived machine learning models were developed from training data and evaluated on an independent evaluation data set. A paired Wilcoxon signed-rank test showed similar performance of manual selection and gaze-derived models on area under the receiver operating characteristic curve (P=.40). Conclusions We used eye tracking to automatically capture physician information-seeking behavior and used it to train models for a LEMR system. The models that were trained using eye tracking performed like models that were trained using manual annotations. These results support further development of eye tracking as a high-throughput method for training clinical decision support systems that prioritize the display of relevant medical record data.

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