Improving Candle Direction Classification in Forex Market Using Support Vector Machine with Hyperparameters Tuning

Oetama, Raymond Sunardi; Heryadi, Yaya; Lukas, Lukas; Suparta, Wayan

doi:10.1109/icic56845.2022.10006993

Cited by 2 publications

(1 citation statement)

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“…This study uses data mining techniques and the CRISP-DM framework to predict the classification [17], [18] of the different forms of anemia. The Naive Bayes, J48 Decision Tree, and Random Forest algorithms will be contrasted.…”

Section: Business Understanding Phasementioning

confidence: 99%

Data Mining Techniques for Predictive Classification of Anemia Disease Subtypes

Setiawan,

Amalia,

Prasetiawan

2024

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Anemia, characterized by insufficient red blood cells or reduced hemoglobin, hinders oxygen transport in the body. Understanding the different types of anemia is vital to tailor effective prevention and treatment. This research explores the role of data mining in predicting and classifying anemia types, focusing on complete blood count (CBC) and demographic data. Data mining is the key to building models that help healthcare professionals diagnose and treat anemia. Employing the cross-industry standard process for data mining (CRISP-DM), with its six phases, facilitates this effort. Our study compared Naïve Bayes, J48 Decision Tree, and Random Forest algorithms using RapidMiner tools, evaluating precision, mean recall, and mean precision. The J48 decision tree outperformed the others, highlighting the importance of algorithm choice in anemia classification models. Furthermore, our analysis identified renal disease-related and chronic anemia as the most prevalent types, with greater occurrence among females. Recognizing gender disparities in the prevalence of anemia informs customized healthcare decisions. Understanding demographic factors in specific types of anemia is crucial to effective care strategies.

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Section: Business Understanding Phasementioning

confidence: 99%