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All Journal IJCIT (Indonesian Journal on Computer and Information Technology) Techno Nusa Mandiri : Journal of Computing and Information Technology JurTI (JURNAL TEKNOLOGI INFORMASI) Computer Science (CO-SCIENCE) JUSTIAN - Jurnal Sistem Informasi Akuntansi Jurnal Insan
Muhammad Rezki
Universitas BSI
Computer Science & IT

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Pendekatan Algoritma Klasifikasi Machine Learning untuk Deteksi Penyakit Demensia Muhammad Iqbal; Hendri Mahmud Nawawi; Muhammad Rezki; Abdul Hamid; Sri Rahayu
Computer Science (CO-SCIENCE) Vol. 3 No. 2 (2023): Juli 2023
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v3i2.1987

Abstract

Early detection of dementia through the use of machine learning classification algorithms is important for providing appropriate interventions to patients. In this context, this study aims to compare the performance of several machine learning classification algorithms in detecting dementia using the attribute selection method. In the early stages, patient data including medical history, cognitive test results, and other attributes were collected as input, an attribute selection algorithm was used to select the most informative attribute subset in detecting dementia. The subset of attributes used as input for training machine learning classification models, several classification algorithms such as Extra Trees (ET), Linear Discriminant Analysis (LDA), Random Forest (RF) and Ridge. In this study, accuracy is used as the main metric to compare algorithm performance. The evaluation results show that the Random Forest (RF) algorithm produces the best performance with an accuracy of 91.56%. The Extra Trees (ET) algorithm has an almost comparable accuracy of 91.44%, while Ridge and Linear Discriminant Analysis (LDA) have an accuracy of 90.44% respectively. In the context of dementia detection, the performance of the Random Forest algorithm with the attribute selection method proved to be the best with an accuracy of 91.56%. These results indicate that the developed model is capable of recognizing complex patterns and relationships between features that are relevant to dementia status. The use of the attribute selection method also contributes to increasing the accuracy and efficiency of the classification algorithm.
Co-Authors Abdul Hamid Abdul Hamid Ahmad Hafidzul Kahfi Ahmad Rifqy Alfiyan Ali Mustopa, Ali Ari Abdillah Badariatul Lailiah Badaritul Lailiah Diah Ayu Ambarsari Doni Purnama Alamsyah Dwiza Riyana Elbiansyah Elbiansyah Hendri Mahmud Nawawi Laksono Trisnantoro Linda Sukmawati Mochammad Abdul Azis Mochammad Rizky Kusumayudha Muhammad Faisal Muhammad Ifan Rifani Ihsan Muhammad Ifan Rifani Ihsan Muhammad Iqbal Muhammad Iqbal Nuraeni Herlinawati Rabiatus Sa'adah Rabiatus Sa’adah Rizky Ade Safitri Safitri Linawati Siti Nurdiani Sri Rahayu winanda winanda