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Muhammad Basit
Universitas Sari Mulia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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PENERAPAN METODE ADABOOST UNTUK MENGOPTIMASI PREDIKSI PENYAKIT STROKE DENGAN ALGORITMA NAÏVE BAYES Agus Byna; Muhammad Basit
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 9, No 3 (2020): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v9i3.1023

Abstract

Stroke is the deadliest disease in the world. Creating injury to this nervous system because of this requires special attention in its treatment. The development of the Industrial Revolution Era 4.0 technology and health science can collaborate to become something beneficial by using machine learning. Currently the benefits are used in predicting several diseases to be anticipated. In particular, stroke using adaboost optimization with naïve Bayes which can provide an accuracy of 0.986 has an Excellent classification diagnosis with 70/30 split data. The previous accuracy without using optimization is 0.976. The result of this accuracy is used by health experts in making good decisions in the field of nursing and medicine. In order to speed up the results of diagnosis to stroke patients.
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