Prediction of hypertention drug therapy response using K-NN imputation and SVM algorithm
Hypertention is a degenerative disease but its healing takes a long time by consuming hypertension drugs until patient's lifetime. The research is conducted to predict response of drug therapy using bioinformatics approach which is a blend of biological and informatics engineering methods. It i...
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| Main Authors: | , , |
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| Format: | EJournal Article |
| Published: |
Institute of Advanced Engineering and Science,
2019-07-01.
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| Subjects: | |
| Online Access: | Get fulltext |
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| LEADER | 02357 am a22003133u 4500 | ||
|---|---|---|---|
| 001 | ijeecs16860_12689 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Muflikhah, Lailil |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Hidayat, Nurul |e author |
| 700 | 1 | 0 | |a Joko Hariyanto, Dimas |e author |
| 245 | 0 | 0 | |a Prediction of hypertention drug therapy response using K-NN imputation and SVM algorithm |
| 260 | |b Institute of Advanced Engineering and Science, |c 2019-07-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/16860 | ||
| 520 | |a Hypertention is a degenerative disease but its healing takes a long time by consuming hypertension drugs until patient's lifetime. The research is conducted to predict response of drug therapy using bioinformatics approach which is a blend of biological and informatics engineering methods. It is used medical record data of hypertensive patient in drug therapy which has an impact on genetic characteristics. The data is constructed as modelling for learning process. Then, it is implemented as a prediction whether the blood presure is under control or not. However, the amount data have no values, then they are required to be applied preprocessing data. Therefore, this research is proposed K-Nearest Neighbor (K-NN) Imputation algorithm for refining data. After that, it is implemented using Support Vector Machine (SVM) algorithm for prediction.The experiment result is achieved the highest accuracy rate of 90% at the best parameter value λ = 0.9, Σ = 2, C = 0.1, ε = 0.001 in ten times iterations. | ||
| 540 | |a Copyright (c) 2019 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |a data mining; machine learning; bioinformatics | ||
| 690 | |a hypertention, missing value, prediction, K-NN Imputation, SVM | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 15, No 1: July 2019; 460-467 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v15.i1 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/16860/12689 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/16860/12689 |z Get fulltext |