PREDICTION OF ANEMIA USING THE PARTICLE SWARM OPTIMIZATION (PSO) AND NAÏVE BAYES ALGORITHM

Septiana tri utami, septiana and Sriyanto, Sriyanto and Handoyo Widi Nugroho, Dr (2024) PREDICTION OF ANEMIA USING THE PARTICLE SWARM OPTIMIZATION (PSO) AND NAÏVE BAYES ALGORITHM.

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Abstract

Health is one of the factors that can influence human activities, but many people rarely or even do not pay attention to their own body health, so that diseases easily come without realizing it, so it is often too late to diagnose the disease they are suffering from. Of the many diseases, one disease that is often diagnosed late is anemia. Implementing the Naïve Bayes and PSO algorithms to predict anemia and evaluating the prediction results using the accuracy parameters of the Naïve Bayes and PSO algorithms. In this research, the Naïve Bayes and PSO algorithms will be applied by optimizing attributes derived from the dataset to predict anemia. PSO can be used to improve model performance or find the best combination of features. PSO can help adjust parameters or select the most informative features to increase accuracy. Naive Bayes model predictions. Once the model is trained, Naive Bayes can be used to predict whether a patient is anemic based on certain features. Naive Bayes calculates the probability for each class based on the given test data. The class with the highest probability will be considered as predicted. Based on the results of testing the Naïve Bayes and PSO algorithm models which were carried out through confusion matrix evaluation, it was proven that the tests carried out by the Naïve Bayes algorithm were 93.88% and the tests carried out with Naïve Bayes and PSO had a high accuracy value, namely 94.02%. The purpose of selecting information acquisition features is to select features or attributes that have a significant influence on anemia. Using PSO can increase a higher level of accuracy. The success of this model makes a positive contribution to efforts to prevent and treat anemia. Keywords: Anemia, Naive Bayes, Particle Swarm Optimization (PSO), Prediction

Item Type:	Article
Subjects:	eBooks eTheses
Divisions:	Pasca Sarjana > Magister Teknik Informatika
Depositing User:	septiana tri utami septiana
Date Deposited:	28 Aug 2024 06:06
Last Modified:	28 Aug 2024 06:06
URI:	http://repo.darmajaya.ac.id/id/eprint/17128

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