Application of Artificial Intelligence Chi-Square Model and Classification Of KNN in Heart Disease Detection
Authors
Rosdiana Rosdiana , Vera Novalia , Ilham Saputra , Mutammimul Ula , Muhammad DanilDOI:
10.31289/jite.v6i1.7343Published:
2022-07-21Issue:
Vol. 6 No. 1 (2022): Issues July 2022Keywords:
KNN, Chi-square, klasifikasi, reduksi, penyakit jantungArticles
Downloads
Downloads
Abstract
Cardiovascular disease is a problem in the blood vessels that do not run smoothly into the heart. This is fatal in patients with a history of heart disease. This problem often occurs in the flow of blood pumps into the heart. The problem examined in this study is how to complete the level of accuracy of each data set and the reduction of each attribute in heart disease. The purpose of this study is to analyze heart disease and classify heart disease using the chi-square and K-Nearest Neighbor algorithms. The results of the study with patient age 57, gender LK, cp 3, trestbps 200, chol 564, fbs 1, restecg 2, thalach 202, oldpeak 6.2, slope 2, ca 4, and the value of thal 3 for the target is there is disease heart 0 or 1 is detected without heart disease when the max-min data is normalized. while to measure the performance of the algorithm with the value of the confusion matrix with the actual class value of 1, prediction class 1 value 44, actual class 0 and prediction class value 6. while the actual class value 0-1, prediction class 1 value 5 and 0-0 value 36. the final stage value of the accuracy measure is 0.87912, the recall value is 0.89797 and the precision value is 0.85714. The implication of the application of the test has an optimal test, the accuracy value with data K = 303 then it can be concluded that based on the test the calculation of the KNN model obtained an accuracy of 91%References
Azis, H., Purnawansyah, P., Fattah, F., & Putri, I. P. (2020). Performa Klasifikasi K-NN dan Cross Validation Pada Data Pasien Pengidap Penyakit Jantung. ILKOM Jurnal Ilmiah, 12(2), 81–86. https://doi.org/10.33096/ilkom.v12i2.507.81-86
DINATA, Rozzi Kesuma; HASDYNA, Novia; ALIF, M. (2021). Applied of Information Gain Algorithm for Culinary Recommendation System in Lhokseumawe. Journal Of Informatics And Telecommunication Engineering, 5(1), 45–52. https://doi.org/https://doi.org/10.31289/jite.v5i1.5199
Duguay, A. T. P. C. (2016). rognostic implications of coronary CT angiography-derived quantitative markers for the prediction of major adverse cardiac events. Journal of Cardiovascular Computed Tomography, 10(6), 458–465.
Frank. (2015). Data Mining –Praktical ML Techniques with Java Implementations, second edition, Morgan Kaufmann Publishers.
Han, J., & M. K. (2012). Data Mining: Concepts and Techniques Second Edition, Elsevier.
Hasdyna, N., & Retno, S. (2022). Purity Algorithm in Determining System of The Productivity of Rice Harvesting Areas in Kabupaten Aceh Utara. JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING, 5(2), 259–267. https://doi.org/10.31289/jite.v5i2.6030
Jiang, F., Ma, L., Broyd, T., & Chen, K. (2021). Digital twin and its implementations in the civil engineering sector. Automation in Construction, 130, 21–23. https://doi.org/10.1016/j.autcon.2021.103838
Jo, Y., Cho, H., Lee, S. Y., Choi, G., Kim, G., Min, H., & Park, Y. (2019). Quantitative Phase Imaging and Artificial Intelligence: A Review. IEEE Journal of Selected Topics in Quantum Electronics, 25(1), 1–14. https://doi.org/10.1109/JSTQE.2018.2859234
Kigka. (2020). Site specific prediction of PCI stenting based on imaging and biomechanics data using gradient boosting tree ensembles. 2812–2815.
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017). Building machines that learn and think like people. Behavioral and Brain Sciences, 40(2012), 1–58. https://doi.org/10.1017/S0140525X16001837
M, U., F, U., I, S., M, M., & Maulana. (2022). Implementation of Machine Learning Using the K-Nearest Neighbor Classification Model in Diagnosing Malnutrition in Children. MULTICA SCIENCE AND TECHNOLOGY (MST) JOURNAL, 2(2). https://doi.org/https://doi.org/10.47002/mst.v2i1.326
Pedersen, T., Johansen, C., & Jøsang, A. (2018). Behavioural Computer Science: an agenda for combining modelling of human and system behaviours. Human-Centric Computing and Information Sciences, 8(1), 1–39. https://doi.org/10.1186/s13673-018-0130-0
Pedoman TataLaksana Gagal Jantung (Edisi Kedu). (2020).
Rahman, A., Fitri, Z., Zulkifli, Z., Ula, M., & Suhendra, B. (2022). Analysis of the Teacher’s Role in Evaluation of Student Learning Performance Using the TOPSIS Model (Case Study of Smk Negeri 1 Lhokseumawe). JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING, 5(2), 452–462. https://doi.org/10.31289/jite.v5i2.6288
Ramadhan, M., & Winata, H. (2019). Sistem Pakar Mendiagnosa Gangguna Fungsi Kardiovaskular Dengan Metode Theorema Bayes. Seminar Nasional Sains Dan Teknologi Informasi (SENSASI), 2(1).
Rambe, A., Tanjung, J. P., & Muhathir, M. (2022). Shafiyyatul Amaliyyah School Student Face Absence Using Principal Component Analysis and K – Nearest Neighbor. JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING, 5(2), 414–422. https://doi.org/10.31289/jite.v5i2.6214
Rozzi, Novia, A. (2021). Applied of Information Gain Algorithm for Culinary Recommendation System in Lhokseumawe. Journal Of Informatics And Telecommunication Engineering, 5(1), 45–52. https://doi.org/10.31289/jite.v5i1.5199
Spencer, R., Thabtah, F., Abdelhamid, N., & Thompson, M. (2020). Exploring feature selection and classification methods for predicting heart disease. DIGITAL HEALTH, 6. https://doi.org/10.1177/2055207620914777
Ula, M, Mauliza, A. (2021). mplementasi Machine Learning Dengan Model Case Based Reasoning Dalam Mendiagnosa Gizi Buruk Pada Anak. Jurnal Informatika Kaputama (JIK), 5(2), 333–339. https://doi.org/https://doi.org/10.1234/jik.v5i2.570
Yusniar, Y., Usman, U., Ula, M., Fakrurrazi, F., Salamah, S., & Q. (2021). Feasibility Strategy on Giving Capital for Salt Farmers in Increasing Economic Productivity Using KNN Classification Model. Journal Mantik, 5(3), 1818–1824.
Author Biography
Rosdiana Rosdiana, Jurusan Teknik elektro Fakultas teknik Universitas Malikussaleh
nohon cepat terbit
Â
License
This work is licensed under aCreative Commons Attribution 4.0 International License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).