Prediksi Stroke Otak Menggunakan Algoritma Naive Bayes dan Particle Swarm Optimization (PSO)

M. Said Hasibuan, Jurusan Magister Teknik Informatika, Fakultas Ilmu Komputer, In and Devi Fransisca, Jurusan Magister Teknik Informatika, Fakultas Ilmu Komputer, In (2024) Prediksi Stroke Otak Menggunakan Algoritma Naive Bayes dan Particle Swarm Optimization (PSO). Prediksi Stroke Otak Menggunakan Algoritma Naive Bayes dan Particle Swarm Optimization (PSO), 9 (1). pp. 109-118. ISSN 2579-566

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Abstract

Health plays an important role in maintaining the quality of human life, but many people ignore this aspect, allowing disease to attack without realizing it, so that diagnosis is often too late. Among the diseases that are often detected too late is stroke, a serious condition that requires immediate treatment because it can cause death in a matter of minutes. According to WHO, stroke is ranked second as the cause of death worldwide after ischemic disease. Data from the Indonesian Ministry of Health shows an increase in stroke cases from 2013 to 2018, with the most vulnerable age range between 55-64 years. Projections also show an increase in the number of stroke cases in 2023. Stroke is also a leading cause of disability in adults. This research aims to produce an effective prediction model for identifying the risk of brain stroke using the Naive Bayes algorithm and Particle Swarm Optimization (PSO). Experiments were conducted to present the model with a focus on the level of accuracy. The results show that using the Naive Bayes algorithm with PSO produces an accuracy rate of 95.02%, increasing accuracy by 8.81% compared to using Naive Bayes independently. This shows that the mixer using PSO is effective in improving the performance of brain stroke prediction models. The combination of the Naive Bayes algorithm with PSO has the potential to help detect the risk of brain stroke earlier, allowing faster intervention and more effective treatment. The results of this research provide an important contribution to the health sector, helping to improve understanding and abilities regarding the prediction of brain stroke. Thus, this research has significant meaning in efforts to prevent and treat this deadly disease.

Item Type:	Article
Subjects:	eDissertations
Divisions:	Artikel Ilmiah Dosen > Fakultas Ilmu Komputer
Depositing User:	Devi Fransisca Fransisca
Date Deposited:	15 Jul 2024 01:25
Last Modified:	15 Jul 2024 01:25
URI:	http://repo.darmajaya.ac.id/id/eprint/16728

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