Deteksi Dini Penyakit Diabetes Menggunakan Algoritma Neural Network Berbasiskan Algoritma Genetika
Abstract
Diabetes or Diabetes Mellitus (DM) is a disease in which the glucose content in the blood can not be processed by the body. Based on the case of many diabetic patients, first action is needed as a solution of the problem of diabetes by predicting to detect early diabetes. This is necessary because often medical decisions are made based on experience and rational reasoning. Prediction of diabetes can be done by using some data of diabetic patients who have been stored in the database to make a pattern for the determination of diabetes with Artificial Intelligen technique so that the result of inaccurate diagnosis can be avoided. In this study the authors apply Genetic Algorithm (GA) to optimize Neural Network (NN) model by searching the best parameter value on Neural Network (NN) model. The experimental results showed a decrease in RMSE value which means an increase in predicted accuracy value, ie from 0.402 +/- 0,035 to 0.396 +/- 0,032.The optimization of the NN model prediction, the relevant policy makers can determine the prediction of diabetes more accurately.
Keywords
Full Text:
References
A. Zamora-Kapoor, A. Fyfe-Johnson, A. Omidpanah, D. Buchwald, and K. Sinclair, “Risk factors for pre-diabetes and diabetes in adolescence and their variability by race and ethnicity,” Prev. Med. (Baltim)., vol. 115, no. August, pp. 47–52, 2018.
A. A. Aljumah, M. G. Ahamad, and M. K. Siddiqui, “Application of data mining: Diabetes health care in young and old patients,” J. King Saud Univ. - Comput. Inf. Sci., vol. 25, no. 2, pp. 127–136, 2012.
H. Wu, S. Yang, Z. Huang, J. He, and X. Wang, “Type 2 diabetes mellitus prediction model based on data mining,” Informatics Med. Unlocked, vol. 10, no. August 2017, pp. 100–107, 2018.
“No Title.” International Diabetes Federation (IDF), 2015.
R. Pramunendar, I. Dewi, and H. Asari, “Penentuan Prediksi Awal Penyakit Jantung Menggunakan Algoritma Back Propagation Neural Network dengan Metode Adaboost,” Semantik, vol. 2013, no. November, pp. 298–304, 2013.
K. Rajesh and V. Sangeetha, “Application of Data Mining Methods and Techniques for Diabetes Diagnosis,” (sem qualis) Int. J. Eng. Res. Innov. Technol., vol. 2, no. 3, pp. 224–229, 2012.
A. Andriani, “Sistem prediksi penyakit diabetes berbasis decision tree,” J. Bianglala Inform., vol. I, no. 1, pp. 1–10, 2013.
S. Kang, “Personalized prediction of drug efficacy for diabetes treatment via patient-level sequential modeling with neural networks,” Artif. Intell. Med., vol. 85, pp. 1–6, 2018.
R. W. Purnamasari, E. Sugiharti, and Dwijanto, “Unnes Journal of Public Health,” UNNES J. Math., vol. 2, no. 2, pp. 0–6, 2013.
O. Somantri and C. Supriyanto, “Algoritme Genetika untuk Peningkatan Prediksi Kebutuhan Permintaan Energi Listrik,” Jnteti, vol. 5, no. 2, pp. 108–114, 2016.
A. M. Zamani and B. Amaliah, “Implementasi Algoritma Genetika pada Struktur Backpropagation Neural Network untuk Klasifikasi Kanker Payudara,” J. Tek. POMITS, vol. 1, no. 1, pp. 1–6, 2012.
F. Fatmawati, “PERBANDINGAN ALGORITMA KLASIFIKASI DATA MINING MODEL C4.5 DAN NAIVE BAYES UNTUK PREDIKSI PENYAKIT DIABETES,” J. Techno Nusa Mandiri, vol. 13, no. 1, pp. 50–59, Mar. 2016.
X.-H. Meng, Y.-X. Huang, D.-P. Rao, Q. Zhang, and Q. Liu, “Comparison of three data mining models for predicting diabetes or prediabetes by risk factors,” Kaohsiung J. Med. Sci., vol. 29, no. 2, pp. 93–99, Feb. 2013.
A. Rohman, V. Suhartono, and C. Supriyanto, “Penerapan algoritma c4.5 berbasis,” vol. 13, pp. 13–19, 2017.
A. Shukla, R. Tiwari, and R. Kala, Real Life Applications of Soft Computing. 2012.
E.D Astuti, Pengantar Jaringan Saraf Tiruan. Wonosobo: Star Publishing, 2009.
M. Badrul, “Optimasi Algoritma Neural Network Dengan Algoritma Genetika Dan Particle Swarm Optimization Untuk Memprediksi Hasil Pemilukada,” J. Pilar Nusa Mandiri, vol. 13, no. 1, pp. 1–11, 2017.
M. Badrul, “Optimasi Neural Network Dengan Algoritma Genetika Untuk Prediksi Hasil Pemilukada,” vol. 3421, pp. 229–242, 2016.
T. Weise, “Global Optimization Algorithms - Theory and Application,” 2007.
D. Sisodia and D. S. Sisodia, “Prediction of Diabetes using Classification Algorithms,” in Procedia Computer Science, 2018, vol. 132, no. Iccids, pp. 1578–1585.
H. Jiawei and M. Kamber, Data mining: concepts and techniques, vol. 5. 2001.
Susanti, S. Martha, and E. Sulistianingsih, “K Nearest Neighbor dalam Imputasi Missing Data,” Bimaster, vol. 7, no. 1, pp. 1–18, 2018.
G. Jiang and W. Wang, “Error estimation based on variance analysis of k-fold cross-validation,” Pattern Recognit., vol. 69, pp. 94–106, 2017.
DOI: https://doi.org/10.30591/jpit.v3i3.1008
Refbacks
This work is licensed under a Creative Commons Attribution 4.0 International License.
JPIT INDEXED BY
 |  |  |  |
 |  |  |  |
 |  | ||
