TY - GEN
T1 - An effective leukemia prediction technique using supervised machine learning classification algorithm
AU - Hossain, Mohammad Akter
AU - Sabik, Mubtasim Islam
AU - Rahman, Md Moshiur
AU - Sakiba, Shadikun Nahar
AU - Muzahidul Islam, A. K.M.
AU - Shatabda, Swakkhar
AU - Islam, Salekul
AU - Ahmed, Ashir
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2021
Y1 - 2021
N2 - Leukemia is not only fatal in nature, the treatment is also extremely expensive. Leukemia’s second stage (typically there are four stages) is enough to blow a large hole in a family’s savings. In this paper, we have designed a supervised machine learning model that accurately predicts the possibility of Leukemia at an early stage. We mainly focus on regular symptoms and the probabilities of a subject to develop Leukemia later on. The parameters or features are usually information available at regular checkups. Firstly, we have defined 17 parameters in consultation with the specialist doctors and then we have collected primary data through surveys of different Leukemia and Non Leukemia patients from hospitals. We have divided the data into train and test datasets and applied different machine learning algorithms such as Decision Tree, Random Forest, KNN, Linear Regression, Adaboost, Naive Bayesian, etc. to find out the accuracy. We obtained 98% of accuracy using Decision Tree and Random Forest, 97.21% using KNN, 91.24% using Logistic Regression, 94.24% using Adaboost, and 75.03% using Naive Bayesian, respectively. It is observed that the Decision Tree and the Random Forest classifier outperform the rest.
AB - Leukemia is not only fatal in nature, the treatment is also extremely expensive. Leukemia’s second stage (typically there are four stages) is enough to blow a large hole in a family’s savings. In this paper, we have designed a supervised machine learning model that accurately predicts the possibility of Leukemia at an early stage. We mainly focus on regular symptoms and the probabilities of a subject to develop Leukemia later on. The parameters or features are usually information available at regular checkups. Firstly, we have defined 17 parameters in consultation with the specialist doctors and then we have collected primary data through surveys of different Leukemia and Non Leukemia patients from hospitals. We have divided the data into train and test datasets and applied different machine learning algorithms such as Decision Tree, Random Forest, KNN, Linear Regression, Adaboost, Naive Bayesian, etc. to find out the accuracy. We obtained 98% of accuracy using Decision Tree and Random Forest, 97.21% using KNN, 91.24% using Logistic Regression, 94.24% using Adaboost, and 75.03% using Naive Bayesian, respectively. It is observed that the Decision Tree and the Random Forest classifier outperform the rest.
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U2 - 10.1007/978-981-33-4673-4_19
DO - 10.1007/978-981-33-4673-4_19
M3 - Conference contribution
AN - SCOPUS:85098288091
SN - 9789813346727
T3 - Advances in Intelligent Systems and Computing
SP - 219
EP - 229
BT - Proceedings of International Conference on Trends in Computational and Cognitive Engineering - Proceedings of TCCE 2020
A2 - Kaiser, M. Shamim
A2 - Bandyopadhyay, Anirban
A2 - Mahmud, Mufti
A2 - Ray, Kanad
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd International Conference on Trends in Computational and Cognitive Engineering, TCCE 2020
Y2 - 17 December 2020 through 18 December 2020
ER -