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Machine Learning-Based Prediction of HIV/AIDS Infection and Treatment Effectiveness: A Clinical Dataset Analysis Jiwa Permana, Agus Aan; Wikranta Arsa, I Gusti Ngurah; Naswin, Ahmad; Sumiyatun
International Journal of Artificial Intelligence in Medical Issues Vol. 3 No. 2 (2025): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v3i2.362

Abstract

The early and accurate prediction of HIV/AIDS infection is critical to improving clinical decision-making and ensuring effective patient management. This study presents a comprehensive machine learning-based approach to predict HIV/AIDS infection status and evaluate the effectiveness of antiretroviral treatments using a well-documented clinical dataset from 1996, comprising 2,139 patient records and 34 features. Through rigorous preprocessing, exploratory data analysis, and feature engineering, several new clinically relevant attributes were constructed, such as CD4/CD8 ratios and immunological change metrics. Four machine learning models—Logistic Regression, Support Vector Machine, Random Forest, and Gradient Boosting—were trained and evaluated. Among these, the Gradient Boosting classifier achieved the highest ROC-AUC score of 0.9335, while Random Forest provided strong predictive performance with a ROC-AUC of 0.9180 and was selected for further evaluation due to its model transparency. Key features influencing infection prediction included CD4+ and CD8+ dynamics, baseline immunological levels, and treatment history. Additionally, the study examined treatment effectiveness by analyzing CD4+ cell count responses across different therapy types. The combination of ZDV and ddI emerged as the most effective regimen, improving immune outcomes and lowering infection rates, while ZDV monotherapy showed the least favorable results. This work underscores the potential of machine learning as a clinical decision support tool in HIV/AIDS care and provides data-driven insights into treatment optimization. Future studies should incorporate longitudinal patient data and real-world clinical environments for broader applicability.
Forecasting Kunjungan Wisatawan Dengan Long Short Term Memory (LSTM) Sugiartawan, Putu; Jiwa Permana, Agus Aan; Prakoso, Paholo Iman
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 1 No 1 (2018): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (965.51 KB) | DOI: 10.33173/jsikti.5

Abstract

Bali is one of the favorite tourist attractions in Indonesia, where the number of foreign tourists visiting Bali is around 4 million over 2015 (Dispar Bali). The number of tourists visiting is spread in various regions and tourist attractions that are located in Bali. Although tourist visits to Bali can be said to be large, the visit was not evenly distributed, there were significant fluctuations in tourist visits. Forecasting or forecasting techniques can find out the pattern of tourist visits. Forecasting technique aims to predict the previous data pattern so that the next data pattern can be known. In this study using the technique of recurrent neural network in predicting the level of tourist visits. One of the techniques for a recurrent neural network (RNN) used in this study is Long Short-Term Memory (LSTM). This model is better than a simple RNN model. In this study predicting the level of tourist visits using the LSTM algorithm, the data used is data on tourist visits to one of the attractions in Bali. The results obtained using the LSTM model amounted to 15,962. The measured value is an error value, with the MAPE technique. The LSTM architecture used consists of 16 units of neuron units in the hidden layer, a learning rate of 0.01, windows size of 3, and the number of hidden layers is 1.
Co-Authors A. A. Gede Yudhi Paramartha Agus Halid, Agus Agus Seputra I Ketut Alkautsar, Yoga Rizky Arditya, I Putu Dion Artha, I Kadek Bayu Danu Artha, I Komang Windra Baskara Nugraha, I Gusti Bagus Darmayasa, Ngakan Nyoman DIATMIKA, KETUT TUTUR Elly Herliyani Erma Susanti Gede Aditra Pradnyana Gede Arya Ardivan Pratama Saputra Gede Nanda Ageng Nugraha Gede Saindra Santyadiputra Gede Wahyu Purnama Gunawan, I Gede Made Deny Surya I Gd Ny Werdyana Guna Mertha I Gusti Agung Putu Bagus Satria Wicaksana I Gusti Ayu Purnamawati I Gusti Ngurah Wikranta Arsa, I Gusti Ngurah I Kadek Nicko Ananda I Kadek Suranata I Ketut Gading I Ketut Purnamawan I Made Ardwi Pradnyana I Made Pageh I Made Putrama I Made Sukarsa I Made Sukarsa I Nyoman Laba Jayanta I Nyoman Saputra Wahyu Wijaya I Nyoman Saputra Wahyu Wijaya Ida Bagus Sebali Mahesa Yogi Ifdil Ifdil Ika Arfiani Kadek Wirahyuni Komang Setemen Kusuma, I Komang Arya Adi Kusumadewi, Ni Putu Ari Made Sudarma Made Sudarma Mahagangga, Komang Adi Satya Marta Dinata, Kadek Prima Giant Naitboho, Okthen Orlanda Naswin, Ahmad Ni Ketut Kertiasih Ni Luh Ita Purnami Ni Putu Dwi Sucita Dartini Ni Putu Novita Puspa Dewi Ni Wayan Marti Octavia, I Gusti Ayu Adiani pande sindu Pande, Satria Imawan Adi Putra Pande Pracasitaram, Gede Made Surya Bumi Pracasitaram, I Gede Made Surya Bumi Prakoso, Paholo Iman Pramudya, Dewa Gede Bhaskara Pranadi Sudhana, I G P Fajar Puridiasta, I Gede Deindra Dwija Putrama, Made Putu Ony Andewi PUTU SUGIARTAWAN Rezania Agramanisti Azdy, Rezania Agramanisti Rukmi Sari Hartati Rukmi Sari Hartati Saputra Wahyu Wijaya Siami, M. Ikbal Sindu, I Gede Partha Sumiyatun Sunia Raharja, I Made Swari, Gusti Putu Ayu Mas Meita Pradnya Tarigan, Thomas Edyson Widodo Prijodiprodjo Wijaya, I Gede Saputra Wahyu Winata, I Gede Arya Wirayani, Made Padmi Witjaksana, Putu Gede Dimas Yoga Rizky Alkautsar Yoga Sucipta, Gede Yudhantara, Kadek Prasta