Improvement Of Prediction Model Using K-Nearest Neighbors (Knn) And K-Means In Medical Data
DOI:
https://doi.org/10.47701/icohetech.v5i1.4175Keywords:
K-Nearest Neighbors (KNN), classification model, clustering, medical datasetAbstract
Improving the performance of a prediction model is very important in its implementation. This study aims to improve the performance of the K-Nearest Neighbors (KNN) classification model with the K-Means clustering algorithm. The dataset used is UCI global data with 300 data and 12 features. The dataset is divided into 200 training data and 100 testing data. The training data is then processed by clustering with K-Means. The cluster centroid from the clustering results will be calculated for its distance from the testing data and produce data classification. The results of the classification process show that the accuracy of the proposed model is 76.45% better when compared to the results of the KNN classification process, for k = 5 the accuracy is 63.37%, k = 10 the accuracy is 64.36% and k = 15 the accuracy is also 64.36%.
References
Arowolo, M.O., Adebiyi, M.O., Adebiyi, A.A. and Olugbara, O. (2021). Optimized Hybrid Investigative Based Dimensionality Reduction Methods for Malaria Vector Using KNN Classifier, Journal of Big Data, 8:29, https://doi.org/10.1186/s40537-021-00415-z
Chen, R., Dewi, C., Su?Wen Huang, S. and Caraka, R.E. (2020). Selecting Critical Features for Data Classification Based on Machine Learning Methods, Journal of Big Data, 7(52). https://doi.org/10.1186/s40537-020-00327-4
Duan, M. (2024). Innovative Compressive Strength Prediction for Recycled Aggregate/Concrete using K?Nearest Neighbors and Meta?Heuristic Optimization Approaches, Journal of Engineering and Applied Science, 71:15, https://doi.org/10.1186/s44147-023-00348-9
Garouani, M., Ahmad, A., Bouneffa, M. and Hamlich, M. (2023). Autoencoder?KNN Meta?Model Based Data Characterization Approach for an Automated Selection of AI Algorithms, Journal of Big Data, 10(14), https://doi.org/10.1186/s40537-023-00687-7
Jadhav, A.D and Pellakuri,V. (2021). Highly Accurate and Efficient Two Phase?Intrusion Detection System (TP?IDS) Using Distributed Processing of HADOOP and Machine Learning Techniques, Journal of Big Data, 8:131, https://doi.org/10.1186/s40537-021-00521-y
Lestari, L. & Sumarlinda, S. (2022). Implementation of K-Nearest Neighbor (KNN) and Suport Vector Machine (SVM) for Clasification Cardiovascular Disease, Multiscience –Vol 2 No 10, January 2022 pp. 30-36.
Musuvathi, A.S.S., Archbald, J.F., Velmurugan, T., Sumathi, D., Devi, S.R. and Preetha, K.S. (2024). Efficient Improvement of Energy Detection Technique in Cognitive Radio Networks Using K?Nearest Neighbour (KNN) Algorithm, EURASIP Journal on Wireless Communications and Networking, 2024:10, pp.10-19, https://doi.org/10.1186/s13638-024-02338-8
Patel, P., Balasubramanian, S. and Annavarapu, R.N. ( 2024). Cross Subject Emotion Identification from Multichannel EEG Sub?Bands using Tsallis Entropy Feature and KNN Classifier, Brain Informatics, 11: 7, https://doi.org/10.1186/s40708-024-00220-3
Rizki, M., Hermawan, A. and Avianto, D. (2024). Optimization of Hyperparameter K in K-Nearest Neighbor Using Particle Swarm Optimization, JUITA: Jurnal Informatika, Vol. 12, No. 1.
Sumarlinda, S., Wijiyanto, Lestari, W. (2022). Decision support system for lecturer publication mapping using k-means clustering method, Journal of Intelligent Decision Support System (IDSS), Vol. 5, No. 4, December 2022, pp. 140-145.
Zhang, L., Du, T. and Jiang, C. (2019). Detection of an Unknown Radio Transmitter Using an Enhanced K-Nearest Neighbor Algorithm Based on Virtual Reference Point and RSSD Information, EURASIP Journal on Wireless Communications and Networking. 2019:71. https://doi.org/10.1186/s13638-019-1383-7
