Optimal Feature Selection in Diabetes Classification Using the MLP Algorithm
| Autor: | Maulana Muhamammad Jogo Samodro, Muhammad Kunta Biddinika, Abdul Fadlil |
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| Jazyk: | English<br />Indonesian |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | IJCCS (Indonesian Journal of Computing and Cybernetics Systems), Vol 18, Iss 2 (2024) |
| Druh dokumentu: | article |
| ISSN: | 1978-1520 2460-7258 |
| DOI: | 10.22146/ijccs.94575 |
| Popis: | In 2021, approximately 531 million people worldwide were affected by diabetes, with 90% diagnosed as type 2. Diabetes often coexists as a comorbidity with other conditions such as kidney and heart disease. The research aims to employ machine learning for diabetes classification, with the Multilayer Perceptron (MLP) algorithm being a key component in the early detection process. The experiments utilized data from the UCI database of Sylhet hospitals, featuring 16 attributes and 2 classes indicating positive and negative diabetes cases. Performance testing using the MLP algorithm involved varying the number of neurons in the hidden layer. The research architecture is denoted as n:p:m, where n represents 16 neurons based on the attributes, m signifies 2 neurons based on the number of classes, and p undergoes variations. The machine learning tool employed in this research is Weka. Within the Weka tool, MLP offers types of hidden layer neuron configurations: 'a', 't', 'i', and 'o'. The test results, conducted with 520 training data and testing on the same dataset, yielded accuracies of 98.85%, 98.85%, 99.42%, and 98.46% for types 'a', 't', 'i', and 'o', respectively. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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