Comparative Study of Lung Disease Prediction System Using Top 10 Data Mining Algorithms with Real Clinical Medical Records

I ketut agung Enriko; Teuku muda Mahuzza; Sevia indah Purnama; Dadang Gunawan

doi:10.2991/978-94-6463-084-8_24

Comparative Study of Lung Disease Prediction System Using Top 10 Data Mining Algorithms with Real Clinical Medical Records

I ketut agung Enriko, Teuku muda Mahuzza, Sevia indah Purnama, Dadang Gunawan

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Proceedings of the First Mandalika International Multi-Conference on Science and Engineering 2022, MIMSE 2022 (Informatics and Computer Science)
Editors	I Gede Pasek Suta Wijaya, Junseok Hwang, Agung Mulyo Widodo, Bambang Irawan
Publisher	Atlantis Press International B.V
Pages	269-281
ISBN (Electronic)	978-94-6463-084-8
ISBN (Print)	978-94-6463-083-1
DOIs	https://doi.org/10.2991/978-94-6463-084-8_24
Publication status	Published - 24 Dec 2022

Keywords

machine learning
lung disease prediction
binary prediction
Naïve Bayes
k-Nearest Neighbor First Section

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10.2991/978-94-6463-084-8_24

Cite this

Enriko, I. K. A., Mahuzza, T. M., Purnama, S. I., & Gunawan, D. (2022). Comparative Study of Lung Disease Prediction System Using Top 10 Data Mining Algorithms with Real Clinical Medical Records. In I. G. P. S. Wijaya, J. Hwang, A. M. Widodo, & B. Irawan (Eds.), Proceedings of the First Mandalika International Multi-Conference on Science and Engineering 2022, MIMSE 2022 (Informatics and Computer Science) (pp. 269-281). Article Chapter 24 Atlantis Press International B.V. https://doi.org/10.2991/978-94-6463-084-8_24

Enriko, I ketut agung ; Mahuzza, Teuku muda ; Purnama, Sevia indah et al. / Comparative Study of Lung Disease Prediction System Using Top 10 Data Mining Algorithms with Real Clinical Medical Records. Proceedings of the First Mandalika International Multi-Conference on Science and Engineering 2022, MIMSE 2022 (Informatics and Computer Science). editor / I Gede Pasek Suta Wijaya ; Junseok Hwang ; Agung Mulyo Widodo ; Bambang Irawan. Atlantis Press International B.V, 2022. pp. 269-281

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title = "Comparative Study of Lung Disease Prediction System Using Top 10 Data Mining Algorithms with Real Clinical Medical Records",

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 Na{\"i}ve Bayes and k-Nearest Neighbor are the best choices among those algorithms in terms of accuracy and speed.",

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author = "Enriko, {I ketut agung} and Mahuzza, {Teuku muda} and Purnama, {Sevia indah} and Dadang Gunawan",

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Enriko, IKA, Mahuzza, TM, Purnama, SI & Gunawan, D 2022, Comparative Study of Lung Disease Prediction System Using Top 10 Data Mining Algorithms with Real Clinical Medical Records. in IGPS Wijaya, J Hwang, AM Widodo & B Irawan (eds), Proceedings of the First Mandalika International Multi-Conference on Science and Engineering 2022, MIMSE 2022 (Informatics and Computer Science)., Chapter 24, Atlantis Press International B.V, pp. 269-281. https://doi.org/10.2991/978-94-6463-084-8_24

Comparative Study of Lung Disease Prediction System Using Top 10 Data Mining Algorithms with Real Clinical Medical Records. / Enriko, I ketut agung; Mahuzza, Teuku muda; Purnama, Sevia indah et al.
Proceedings of the First Mandalika International Multi-Conference on Science and Engineering 2022, MIMSE 2022 (Informatics and Computer Science). ed. / I Gede Pasek Suta Wijaya; Junseok Hwang; Agung Mulyo Widodo; Bambang Irawan. Atlantis Press International B.V, 2022. p. 269-281 Chapter 24.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Comparative Study of Lung Disease Prediction System Using Top 10 Data Mining Algorithms with Real Clinical Medical Records

AU - Enriko, I ketut agung

AU - Mahuzza, Teuku muda

AU - Purnama, Sevia indah

AU - Gunawan, Dadang

PY - 2022/12/24

Y1 - 2022/12/24

N2 - 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.

AB - 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.

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BT - Proceedings of the First Mandalika International Multi-Conference on Science and Engineering 2022, MIMSE 2022 (Informatics and Computer Science)

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Enriko IKA, Mahuzza TM, Purnama SI, Gunawan D. Comparative Study of Lung Disease Prediction System Using Top 10 Data Mining Algorithms with Real Clinical Medical Records. In Wijaya IGPS, Hwang J, Widodo AM, Irawan B, editors, Proceedings of the First Mandalika International Multi-Conference on Science and Engineering 2022, MIMSE 2022 (Informatics and Computer Science). Atlantis Press International B.V. 2022. p. 269-281. Chapter 24 doi: 10.2991/978-94-6463-084-8_24

Comparative Study of Lung Disease Prediction System Using Top 10 Data Mining Algorithms with Real Clinical Medical Records

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Keywords

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