Implementation of Artificial Bee Colony-Ensemble in Predicting Side Effects: Case Study Reproductive System and Breast Disorders - Dalam bentuk pengganti sidang - Artikel Jurnal
NABILA AURELLIA
Informasi Umum
Kode
25.04.533
Klasifikasi
000 - General Works
Jenis
Karya Ilmiah - Skripsi (S1) - Reference
Subjek
Machine Learning
Dilihat
65 kali
Informasi Lainnya
Abstraksi
According to the FDA, drugs are substances intended to diagnose, cure, alleviate, treat, or prevent diseases. Despite rigorous clinical trials, adverse drug reactions (ADRs) rank among the top 10 leading causes of death in some countries. Conventional drug research methods involving in vivo and in vitro testing require significant time and resources, prompting the need for efficient computational approaches. This study introduces a novel integration of the Artificial Bee Colony (ABC) algorithm with ensemble models for feature selection and ADR prediction, specifically focusing on reproductive system and breast disorders. By leveraging data from the Side Effect Resource (SIDER), three ensemble techniques were evaluated: Random Forest, AdaBoost, and XGBoost. The ABC algorithm optimized feature sets, and hyperparameter tuning further enhanced model performance. Random Forest demonstrated superior performance, achieving an accuracy of 0.6311 and an F1-Score of 0.6770. These results highlight the potential of the
Koleksi & Sirkulasi
Tersedia 1 dari total 1 Koleksi
Anda harus log in untuk mengakses flippingbook
Pengarang
| Nama | NABILA AURELLIA |
| Jenis | Perorangan |
| Penyunting | Isman Kurniawan |
| Penerjemah |
Penerbit
| Nama | Universitas Telkom, S1 Informatika |
| Kota | Bandung |
| Tahun | 2025 |
Sirkulasi
| Harga sewa | IDR 0,00 |
| Denda harian | IDR 0,00 |
| Jenis | Non-Sirkulasi |
