Infolitika Journal of Data Science最新文献

Ensemble Machine Learning Approach for Quantitative Structure Activity Relationship Based Drug Discovery: A Review 基于定量结构活性关系的药物发现集成机器学习方法综述

Infolitika Journal of Data Science Pub Date : 2023-09-25 DOI: 10.60084/ijds.v1i1.91

Teuku Rizky Noviandy, Aga Maulana, Ghazi Mauer Idroes, Talha Bin Emran, Trina Ekawati Tallei, Zuchra Helwani, Rinaldi Idroes

{"title":"Ensemble Machine Learning Approach for Quantitative Structure Activity Relationship Based Drug Discovery: A Review","authors":"Teuku Rizky Noviandy, Aga Maulana, Ghazi Mauer Idroes, Talha Bin Emran, Trina Ekawati Tallei, Zuchra Helwani, Rinaldi Idroes","doi":"10.60084/ijds.v1i1.91","DOIUrl":"https://doi.org/10.60084/ijds.v1i1.91","url":null,"abstract":"This comprehensive review explores the pivotal role of ensemble machine learning techniques in Quantitative Structure-Activity Relationship (QSAR) modeling for drug discovery. It emphasizes the significance of accurate QSAR models in streamlining candidate compound selection and highlights how ensemble methods, including AdaBoost, Gradient Boosting, Random Forest, Extra Trees, XGBoost, LightGBM, and CatBoost, effectively address challenges such as overfitting and noisy data. The review presents recent applications of ensemble learning in both classification and regression tasks within QSAR, showcasing the exceptional predictive accuracy of these techniques across diverse datasets and target properties. It also discusses the key challenges and considerations in ensemble QSAR modeling, including data quality, model selection, computational resources, and overfitting. The review outlines future directions in ensemble QSAR modeling, including the integration of multi-modal data, explainability, handling imbalanced data, automation, and personalized medicine applications while emphasizing the need for ethical and regulatory guidelines in this evolving field.","PeriodicalId":486036,"journal":{"name":"Infolitika Journal of Data Science","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135866812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Maternal and Child Healthcare Services in Aceh Province, Indonesia: A Correlation and Clustering Analysis in Statistics 妇幼保健服务在亚齐省，印度尼西亚:统计的相关性和聚类分析

Infolitika Journal of Data Science Pub Date : 2023-09-25 DOI: 10.60084/ijds.v1i1.88

Novi Reandy Sasmita, Siti Ramadeska, Reksi Utami, Zuhra Adha, Ulayya Putri, Risky Haezah Syarafina, La Ode Reskiaddin, Saiful Kamal, Yarmaliza Yarmaliza, Muliadi Muliadi, Arif Saputra

{"title":"Maternal and Child Healthcare Services in Aceh Province, Indonesia: A Correlation and Clustering Analysis in Statistics","authors":"Novi Reandy Sasmita, Siti Ramadeska, Reksi Utami, Zuhra Adha, Ulayya Putri, Risky Haezah Syarafina, La Ode Reskiaddin, Saiful Kamal, Yarmaliza Yarmaliza, Muliadi Muliadi, Arif Saputra","doi":"10.60084/ijds.v1i1.88","DOIUrl":"https://doi.org/10.60084/ijds.v1i1.88","url":null,"abstract":"Infant mortality remains a public health problem in Aceh Province, Indonesia. Health services during pregnancy are an essential factor in reducing infant mortality. Studies examining factors such as maternal and child health services that have implications for infant mortality in Aceh province are still scarce. Therefore, this study aims to examine the correlation between maternal and child health services variables such as Blood-Supplementing Tablets (TTD), Coverage of the First Visit of Pregnant Women (K1), Coverage of the First Visit of Pregnant Women (K4), and management of Obstetric Complications to live births and to map the maternal and child health services obtained during pregnancy. A cross-sectional study was used as the research study. This study used descriptive statistics, such as measures of data centering and data dispersion. In this work, inferential statistical analysis was conducted using the Shapiro-Wilk test, Spearman test, and fuzzy c-means. The result of the Shapiro Wilk test stated that the live birth rate variable and all Maternal and Child Healthcare Services variables were not normally distributed (p-value < 0.05), all Maternal and Child Healthcare Services variables were positively correlated to live birth rate based on the Spearman test (p-value < 0.05). Based on the Silhouette Index with 0.555, the formation of 3 clusters is the optimal cluster. The clustering is based on the Maternal and Child Healthcare Services that have been provided, where the first, second, and third clusters consist of five districts/city, eight districts/city, and ten districts/city, respectively, as a result of Fuzzy C-Means Clustering.","PeriodicalId":486036,"journal":{"name":"Infolitika Journal of Data Science","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135866811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Implementation of Hybrid CNN-XGBoost Method for Leukemia Detection Problem 混合CNN-XGBoost方法在白血病检测中的实现

Infolitika Journal of Data Science Pub Date : 2023-09-24 DOI: 10.60084/ijds.v1i1.87

Taufiq Hidayat, Edrian Hadinata, Irfan Sudahri Damanik, Zakial Vikki, Irvanizam Irvanizam

{"title":"Implementation of Hybrid CNN-XGBoost Method for Leukemia Detection Problem","authors":"Taufiq Hidayat, Edrian Hadinata, Irfan Sudahri Damanik, Zakial Vikki, Irvanizam Irvanizam","doi":"10.60084/ijds.v1i1.87","DOIUrl":"https://doi.org/10.60084/ijds.v1i1.87","url":null,"abstract":"Leukemia is a blood cancer in which blood cells become malignant and uncontrolled. It can cause damage to the function of the body's organs. Several machine learning methods have been used to automatically detect biomedical images, including blood cell images. In this study, we utilized a hybrid machine learning method, called a hybrid Convolutional Neural Network-eXtreme Gradient Boosting (CNN-XGBoost) method to detect leukemia in blood cells. The hybrid method combines two machine learning methods. We use CNN as the basic classifier and XGBoost as the main classification method. The aim of this methodology was to assess whether incorporating the basic classification method would lead to an enhancement in the performance of the main classification model. The experimental findings demonstrated that the utilization of XGBoost as the main classifier led to a marginal increase in accuracy, elevating it from 85.32% to 85.43% compared to the basic CNN classification. This research highlights the potential of hybrid machine learning approaches in biomedical image analysis and their role in advancing the early diagnosis of leukemia and potentially other medical conditions.","PeriodicalId":486036,"journal":{"name":"Infolitika Journal of Data Science","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135926595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ANFIS-Based QSRR Modelling for Kovats Retention Index Prediction in Gas Chromatography 基于anfiss的气相色谱中Kovats保留指数预测的QSRR模型

Infolitika Journal of Data Science Pub Date : 2023-09-07 DOI: 10.60084/ijds.v1i1.73

Rinaldi Idroes, Teuku Rizky Noviandy, Aga Maulana, Rivansyah Suhendra, Novi Reandy Sasmita, Muslem Muslem, Ghazi Mauer Idroes, Raudhatul Jannah, Razief Perucha Fauzie Afidh, Irvanizam Irvanizam

引用次数: 1

Machine Learning Approach for Diabetes Detection Using Fine-Tuned XGBoost Algorithm 基于微调XGBoost算法的糖尿病检测机器学习方法

Infolitika Journal of Data Science Pub Date : 2023-08-22 DOI: 10.60084/ijds.v1i1.72

Aga Maulana, Farassa Rani Faisal, Teuku Rizky Noviandy, Tatsa Rizkia, Ghazi Mauer Idroes, Trina Ekawati Tallei, Mohamed El-Shazly, Rinaldi Idroes

引用次数: 5