Studi Komperasi prediksi Penyakit Paru-Paru Menggunakan 10 algoritma Machine Learning Dengan Data Riil Dari Rumah Sakit Cut Meuthia

Teuku, Muda Mahuzza F (2023) Studi Komperasi prediksi Penyakit Paru-Paru Menggunakan 10 algoritma Machine Learning Dengan Data Riil Dari Rumah Sakit Cut Meuthia. Undergraduate Thesis thesis, Institut Teknologi Telkom Purwokerto.

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Abstract

These years, the use of machine learning for disease prediction is blooming. Meanwhile, lung disease is one of the deadliest diseases in the world. Many researchers have been doing research on lung disease predictions using various techniques. In this study, ten machine learning algorithms are used for comparative study in lung disease prediction. The dataset is collected from a hospital in Banda Aceh, Indonesia, consisting of 300 data. The parameters included in the dataset are: symptoms, body temperature, respiration rate, oxygen saturation, blood pressure, heart rate, sex, and age. This dataset needs to be pre-processed and then analyzed using those top 10 machine learning algorithms. The prediction will be whether a patient gets a lung disease or not (binary prediction). The result shows that Naïve Bayes and k-Nearest Neighbor are the best choices among those algorithms in terms of accuracy and speed Keywords : machine learning, lung disease prediction, binary prediction, Naïve Bayes, k-Nearest Neighbor First Section

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