Analysis of the random forest and grid search algorithms in early detection of diabetes mellitus disease
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Published:
Aug 30, 2023
Keywords:
Diabetes Mellitus, Grid Search Algorithm, Prediction system, Random forest algorithm
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Computer Science
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Abstract
This research focuses on implementing the Random Forest and Grid Search algorithms for the early detection of diabetes mellitus, aiming to modernize and enhance medical practices using technology. The proposed model achieved an accuracy of 77.06%, a precision of 71.43%, a recall of 47.30%, and a misclassification error of 22.94%. Comparative analysis with other data mining algorithms, including Decision Tree, Random Forest without Grid Search, and Cat Boost, demonstrated that the Random Forest with Grid Search algorithm outperformed the others. By utilizing Grid Search, the accuracy of the Random Forest algorithm increased by 2.03%. These findings indicate the potential effectiveness of machine learning in early diabetes detection. While the research offers promising results, there are limitations in terms of the dataset size and the number of detection variables used. Future studies should explore larger datasets and alternative algorithms to further enhance accuracy and aid in the early detection of diabetes mellitus.
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How to Cite
Andi, A., Thamrin, T., Susanto, A. ., Wijaya, E. . and Djohan, D. . (2023) “Analysis of the random forest and grid search algorithms in early detection of diabetes mellitus disease”, Jurnal Mantik, 7(2), pp. 1117-1124. doi: 10.35335/mantik.v7i2.3981.
References
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Andi, Juliandy, C., Robet, R., Pribadi, O., & Wijaya, R. (2021). Image Authentication Application with Blockchain to Prevent and Detect Image Plagiarism. 2021 6th International Conference on Informatics and Computing, ICIC 2021, December. https://doi.org/10.1109/ICIC54025.2021.9632966
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Anissa, K., Rumahorbo, H., & Wahyuni, S. (2023). Development of Instruments Test to Detect Diabetes Mellitus in Pregnancy. Jurnal Kebidanan, 12(1), 27–36. https://doi.org/10.26714/jk.12.1.2023.27-36
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Ikhromr, F. N., Sugiyarto, I., Faddillah, U., & Sudarsono, B. (2023). Implementasi Data Mining Untuk Memprediksi Penyakit Diabetes Menggunakan Algoritma Naives Bayes dan K-Nearest Neighbor. INTECOMS: Journal of Information Technology and Computer Science, 6(1), 416–428.
Iparraguirre-villanueva, O., Espinola-linares, K., Ornella, R., Castañeda, F., & Cabanillas-carbonell, M. (2023). Application of Machine Learning Models for Early Detection and Accurate Classification of Type 2 Diabetes.
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Nugroho, A., & Amrullah, A. (2023). EVALUASI KINERJA ALGORITMA K-NN MENGGUNAKAN K-FOLD CROSS VALIDATION PADA DATA DEBITUR KSP GALIH MANUNGGAL. Jurnal Informatika Teknologi Dan Sains (JINTEKS), 5(2), 294–300.
Pamuji, F. Y., & Ramadhan, V. P. (2021). Komparasi Algoritma Random Forest dan Decision Tree untuk Memprediksi Keberhasilan Immunotheraphy. Jurnal Teknologi Dan Manajemen Informatika, 7(1), 46–50. https://doi.org/10.26905/jtmi.v7i1.5982
Perdana, A., Hermawan, A., & Avianto, D. (2023). Analyze Important Features of PIMA Indian Database For Diabetes Prediction Using KNN. Jurnal Sisfokom (Sistem Informasi Dan Komputer), 12(1), 70–75. https://doi.org/10.32736/sisfokom.v12i1.1598
Prasetya, M. R. A., & Priyatno, A. M. (2023). Penanganan Imputasi Missing Values pada Data Time Series dengan Menggunakan Metode Data Mining. Jurnal Informasi Dan Teknologi, 5(2), 56–62. https://doi.org/10.37034/jidt.v5i1.324
Putri, T. A. E., Widiharih, T., & Santoso, R. (2023). Penerapan Tuning Hyperparameter Randomsearchcv Pada Adaptive Boosting Untuk Prediksi Kelangsungan Hidup Pasien Gagal Jantung. Jurnal Gaussian, 11(3), 397–406. https://doi.org/10.14710/j.gauss.11.3.397-406
Ramadhan, M. M., Sitanggang, I. S., Nasution, F. R., & Ghifari, A. (2017). Parameter Tuning in Random Forest Based on Grid Search Method for Gender Classification Based on Voice Frequency. DEStech Transactions on Computer Science and Engineering, cece. https://doi.org/10.12783/dtcse/cece2017/14611
Ramayu, I. M. S., Susanto, F., & Mahendra, G. S. (2022). Penerapan Data Mining Dengan Algoritma C4.5 Dalam Pemesanan Obat Guna Meningkatkan Keuntungan Apotek. Prosiding Seminar Nasional Manajemen, Desain & Aplikasi Bisnis Teknologi (SENADA), 5, 237–245. http://senada.idbbali.ac.id
Robet, Juliandy, C., Andi, Hendri, Hendrik, J., & Tarigan, F. A. (2022). Image Road Surface Classification Based on GLCM Feature Using LGBM Classifier. IOP Conference Series: Earth and Environmental Science, 1083(1). https://doi.org/10.1088/1755-1315/1083/1/012006
Sari, L., Romadloni, A., & Listyaningrum, R. (2023). Penerapan Data Mining dalam Analisis Prediksi Kanker Paru Menggunakan Algoritma Random Forest. Infotekmesin, 14(1), 155–162. https://doi.org/10.35970/infotekmesin.v14i1.1751
Siregar, S. D., Uli, Y. R. G., Sintami, N., Butar-butar, H. S., & Simanjuntak, R. M. (2023). Implementation of KNN algorithm in classifying diabetic ulcers in patients with diabetes mellitus. Jurnal Mantik, 7(2), 691–701.
Sriyanto, & Supriyatna, A. R. (2023). Prediksi Penyakit Diabetes Menggunakan Algoritma Random Forest. Teknika, 17(1), 163–172.
Wahyu, B. A. R. P., Fayi, F. A., Mahendra, C. P., & Hapsari, R. K. (2023). Klasifikasi Penderita Penyakit Diabetes Menggunakan Algoritma Decision Tree C4.5. Journal of Information Technology, 8(1), 80–89. https://doi.org/10.47970/siskom-kb.v4i1.173
Wirasasmita, D., & Anisa, E. (2023). Analisis Sentiment Twitter Berbasis Grid Search Algorithm (GSA) dengan Metode Support Vector Machine (SVM). Asiimetrik, 5(1), 35–42.
Yusnaeni, W., & Widiarina. (2022). Penerapan Algoritma C4.5 Dalam Prediksi Resiko Diabetes Tahap Awal (Early Stage Diabetes). Jurnal Teknik Komputer AMIK BSI, 8(1), 56–60. https://doi.org/10.31294/jtk.v4i2
Alhabib, I., Faqih, A., & Dikananda, F. (2022). Komparasi Metode Deep Learning, Naïve Bayes Dan Random Forest Untuk Prediksi Penyakit Jantung. INFORMATICS FOR EDUCATORS AND PROFESSIONAL?: Journal of Informatics, 6(2), 176–185. https://doi.org/10.51211/itbi.v6i2.1881
Ambarwari, A., Jafar Adrian, Q., & Herdiyeni, Y. (2020). Analysis of the Effect of Data Scaling on the Performance of the Machine Learning Algorithm for Plant Identification. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 4(1), 117–122. https://doi.org/10.29207/resti.v4i1.1517
Andi, A., Juliandy, C., & David, D. (2023). Clustering Analysis of Tweets About COVID-19 Using the K-Means Algorithm. Sinkron, 8(1), 543–533. https://doi.org/10.33395/sinkron.v8i1.12145
Andi, Juliandy, C., Robet, R., Pribadi, O., & Wijaya, R. (2021). Image Authentication Application with Blockchain to Prevent and Detect Image Plagiarism. 2021 6th International Conference on Informatics and Computing, ICIC 2021, December. https://doi.org/10.1109/ICIC54025.2021.9632966
Anggoro, D. A., & Afdallah, N. A. (2022). Grid Search CV Implementation in Random Forest Algorithm to Improve Accuracy of Breast Cancer Data. International Journal on Advanced Science, Engineering and Information Technology, 12(2), 515–520. https://doi.org/10.18517/ijaseit.12.2.15487
Anissa, K., Rumahorbo, H., & Wahyuni, S. (2023). Development of Instruments Test to Detect Diabetes Mellitus in Pregnancy. Jurnal Kebidanan, 12(1), 27–36. https://doi.org/10.26714/jk.12.1.2023.27-36
Apriliah, W., Kurniawan, I., Baydhowi, M., & Haryati, T. (2021). SISTEMASI: Jurnal Sistem Informasi Prediksi Kemungkinan Diabetes pada Tahap Awal Menggunakan Algoritma Klasifikasi Random Forest. Jurnal Sistem Informasi, 10(1), 163–171. http://sistemasi.ftik.unisi.ac.id
Dikka, G., Prana, W., & Gede, L. (2023). Analisis Performa Algoritma K-Nearest Neighbor dalam Klasifikasi Tingkat Kerontokan Rambut. Jurnal Nasional Teknologi Informasi Dan Aplikasinya, 1(3), 941–950.
Fatmawati, & Rifai, N. A. K. (2023). Klasifikasi Penyakit Diabetes Retinopati Menggunakan Support Vector Machine dengan Algoritma Grid Search Cross-Validation. Jurnal Riset Statistika (JRS), 3(1), 79–86.
Ikhromr, F. N., Sugiyarto, I., Faddillah, U., & Sudarsono, B. (2023). Implementasi Data Mining Untuk Memprediksi Penyakit Diabetes Menggunakan Algoritma Naives Bayes dan K-Nearest Neighbor. INTECOMS: Journal of Information Technology and Computer Science, 6(1), 416–428.
Iparraguirre-villanueva, O., Espinola-linares, K., Ornella, R., Castañeda, F., & Cabanillas-carbonell, M. (2023). Application of Machine Learning Models for Early Detection and Accurate Classification of Type 2 Diabetes.
Karrar, A. E. (2022). The Effect of Using Data Pre-Processing by Imputations in Handling Missing Values. Indonesian Journal of Electrical Engineering and Informatics, 10(2), 375–384. https://doi.org/10.52549/ijeei.v10i2.3730
Kohsasih, K. L., Hayadi, B. H., Robet, Juliandy, C., Pribadi, O., & Andi. (2022). Sentiment Analysis for Financial News Using RNN-LSTM Network. 2022 4th International Conference on Cybernetics and Intelligent System, ICORIS 2022. https://doi.org/10.1109/ICORIS56080.2022.10031595
Napiah, M., Astuti, R. D., & Pratama, E. K. (2023). Komparasi Algoritma Machine Learning untuk Klasifikasi Gejala Coronavirus Disease 19 ( Covid-19 ). Computer Science (CO-SCIENCE), 3(2), 78–83.
Nugroho, A., & Amrullah, A. (2023). EVALUASI KINERJA ALGORITMA K-NN MENGGUNAKAN K-FOLD CROSS VALIDATION PADA DATA DEBITUR KSP GALIH MANUNGGAL. Jurnal Informatika Teknologi Dan Sains (JINTEKS), 5(2), 294–300.
Pamuji, F. Y., & Ramadhan, V. P. (2021). Komparasi Algoritma Random Forest dan Decision Tree untuk Memprediksi Keberhasilan Immunotheraphy. Jurnal Teknologi Dan Manajemen Informatika, 7(1), 46–50. https://doi.org/10.26905/jtmi.v7i1.5982
Perdana, A., Hermawan, A., & Avianto, D. (2023). Analyze Important Features of PIMA Indian Database For Diabetes Prediction Using KNN. Jurnal Sisfokom (Sistem Informasi Dan Komputer), 12(1), 70–75. https://doi.org/10.32736/sisfokom.v12i1.1598
Prasetya, M. R. A., & Priyatno, A. M. (2023). Penanganan Imputasi Missing Values pada Data Time Series dengan Menggunakan Metode Data Mining. Jurnal Informasi Dan Teknologi, 5(2), 56–62. https://doi.org/10.37034/jidt.v5i1.324
Putri, T. A. E., Widiharih, T., & Santoso, R. (2023). Penerapan Tuning Hyperparameter Randomsearchcv Pada Adaptive Boosting Untuk Prediksi Kelangsungan Hidup Pasien Gagal Jantung. Jurnal Gaussian, 11(3), 397–406. https://doi.org/10.14710/j.gauss.11.3.397-406
Ramadhan, M. M., Sitanggang, I. S., Nasution, F. R., & Ghifari, A. (2017). Parameter Tuning in Random Forest Based on Grid Search Method for Gender Classification Based on Voice Frequency. DEStech Transactions on Computer Science and Engineering, cece. https://doi.org/10.12783/dtcse/cece2017/14611
Ramayu, I. M. S., Susanto, F., & Mahendra, G. S. (2022). Penerapan Data Mining Dengan Algoritma C4.5 Dalam Pemesanan Obat Guna Meningkatkan Keuntungan Apotek. Prosiding Seminar Nasional Manajemen, Desain & Aplikasi Bisnis Teknologi (SENADA), 5, 237–245. http://senada.idbbali.ac.id
Robet, Juliandy, C., Andi, Hendri, Hendrik, J., & Tarigan, F. A. (2022). Image Road Surface Classification Based on GLCM Feature Using LGBM Classifier. IOP Conference Series: Earth and Environmental Science, 1083(1). https://doi.org/10.1088/1755-1315/1083/1/012006
Sari, L., Romadloni, A., & Listyaningrum, R. (2023). Penerapan Data Mining dalam Analisis Prediksi Kanker Paru Menggunakan Algoritma Random Forest. Infotekmesin, 14(1), 155–162. https://doi.org/10.35970/infotekmesin.v14i1.1751
Siregar, S. D., Uli, Y. R. G., Sintami, N., Butar-butar, H. S., & Simanjuntak, R. M. (2023). Implementation of KNN algorithm in classifying diabetic ulcers in patients with diabetes mellitus. Jurnal Mantik, 7(2), 691–701.
Sriyanto, & Supriyatna, A. R. (2023). Prediksi Penyakit Diabetes Menggunakan Algoritma Random Forest. Teknika, 17(1), 163–172.
Wahyu, B. A. R. P., Fayi, F. A., Mahendra, C. P., & Hapsari, R. K. (2023). Klasifikasi Penderita Penyakit Diabetes Menggunakan Algoritma Decision Tree C4.5. Journal of Information Technology, 8(1), 80–89. https://doi.org/10.47970/siskom-kb.v4i1.173
Wirasasmita, D., & Anisa, E. (2023). Analisis Sentiment Twitter Berbasis Grid Search Algorithm (GSA) dengan Metode Support Vector Machine (SVM). Asiimetrik, 5(1), 35–42.
Yusnaeni, W., & Widiarina. (2022). Penerapan Algoritma C4.5 Dalam Prediksi Resiko Diabetes Tahap Awal (Early Stage Diabetes). Jurnal Teknik Komputer AMIK BSI, 8(1), 56–60. https://doi.org/10.31294/jtk.v4i2
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