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All Journal Heca Journal of Applied Sciences Infolitika Journal of Data Science
Noviana Husdayanti
Teuku Umar Hospital, Aceh Jaya 23654, Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Engineering Health Professions Medicine & Pharmacology

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Evaluation of Gradient Boosted Classifier in Atopic Dermatitis Severity Score Classification Rivansyah Suhendra; Suryadi Suryadi; Noviana Husdayanti; Aga Maulana; Teuku Rizky Noviandy; Novi Reandy Sasmita; Muhammad Subianto; Nanda Earlia; Nurdjannah Jane Niode; Rinaldi Idroes
Heca Journal of Applied Sciences Vol. 1 No. 2 (2023): October 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/hjas.v1i2.85

Abstract

This study investigates the application of the Gradient Boosting machine learning technique to enhance the classification of Atopic Dermatitis (AD) skin disease images, reducing the potential for manual classification errors. AD, also known as eczema, is a common and chronic inflammatory skin condition characterized by pruritus (itching), erythema (redness), and often lichenification (thickening of the skin). AD affects individuals of all ages and significantly impacts their quality of life. Accurate and efficient diagnostic tools are crucial for the timely management of AD. To address this need, our research encompasses a multi-step approach involving data preprocessing, feature extraction using various color spaces and evaluating classification outcomes through Gradient Boosting. The results demonstrate an accuracy of 93.14%. This study contributes to the field of dermatology by providing a robust and reliable tool to support dermatologists in identifying AD skin disease, facilitating timely intervention and improved patient care.
Cardiovascular Disease Prediction Using Gradient Boosting Classifier Rivansyah Suhendra; Noviana Husdayanti; Suryadi Suryadi; Ilham Juliwardi; Sanusi Sanusi; Abdurrahman Ridho; Muhammad Ardiansyah; Murhaban Murhaban; Ikhsan Ikhsan
Infolitika Journal of Data Science Vol. 1 No. 2 (2023): December 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v1i2.131

Abstract

Cardiovascular Disease (CVD), a prevalent global health concern involving heart and blood vessel disorders, prompts this research's focus on accurate prediction. This study explores the predictive capabilities of the Gradient Boosting Classifier (GBC) in cardiovascular disease across two datasets. Through meticulous data collection, preprocessing, and GBC classification, the study achieves a noteworthy accuracy of 97.63%, underscoring the GBC's effectiveness in accurate CVD detection. The robust performance of the GBC, evidenced by high accuracy, highlights its adaptability to diverse datasets and signifies its potential as a valuable tool for early identification of cardiovascular diseases. These findings provide valuable insights into the application of machine learning methodologies, particularly the GBC, in advancing the accuracy of CVD prediction, with implications for proactive healthcare interventions and improved patient outcomes.
Co-Authors Abdurrahman Ridho Aga Maulana Ikhsan Ikhsan Ilham Juliwardi Muhammad Ardiansyah Muhammad Subianto Murhaban Nanda Earlia Novi Reandy Sasmita Nurdjannah Jane Niode Rinaldi Idroes Rivansyah Suhendra Sanusi Sanusi Suryadi Suryadi Teuku Rizky Noviandy