The optimal time slot selection and feature selection for the prediction of drugs for diseases

Document Type : Research Paper

Authors

Department of Computer Science, Karpagam Academy of Higher Education (Deemed to be University), Coimbatore-21, Tamilnadu, India

Abstract

Data mining techniques have been applied to analyze, predict and diagnose diseases. The prediction of disease becomes meaningless when there is no proper recommendation of a drug to the patient. A drug recommendation method called Artificial Neural Network (ANN) with side effect constraints was proposed to recommend drug names for multiple diseases such as Chronic Kidney Disease (CKD), diabetic and heart disease based on the interaction between drug and disease and their side effects. In this drug recommendation method, multiple attributes of drugs and patients were collected from different sources and the hidden relationship between the attributes was predicted by using a Hidden Markov Model (HMM). In addition to this, statistical features were calculated and added as additional features. The collected and calculated features were used in ANN with side effect constraint classifier which predicted drug name for multiple diseases with the consideration of side effects. However, there is a high dimensionality problem in the recommended method due to more number of features. Moreover, it leads to more computational and space complexity in the ANN classifier. In this paper, an efficient Krill Herd (KH) algorithm for optimization is introduced to solve the above-mentioned problems in the drug recommendation method. According to the herding behavior of the likeness of the krill individuals, KH selects the optimal features. The multiple attributes of drugs and patients are collected in a different time slots. The KH algorithm is also used to select the optimal time slot. Then, the optimal time slot and features are given as input to ANN which predicts drug names for multiple diseases with high accuracy and low computational complexity.

Keywords

[1] M. Aladeemy, S. Tutun and M.T. Khasawneh, A new hybrid approach for feature selection and support vector
machine model selection based on self-adaptive cohort intelligence, Expert Systems with Applications, 88 (2017)
118–131.[2] K. Buzaand and L. Peska, Drug–target interaction prediction with Bipartite Local Models and hubness-aware
regression, Neurocomput. 260 (2017) 284–293.
[3] R. Celebi, O. Erten and M. Dumontier, Machine Learning Based Drug Indication Prediction Using Linked Open
Data, SWAT4LS, 2017.
[4] A.H. Gandomi and A.H. Alavi, Krill herd: a new bio-inspired optimization algorithm, Commun. Nonlinear Sci.
Numerical Simul. 17(12) (2012) 4831–484.
[5] S. Gunasundari, S. Janakiraman and S. Meenambal, Velocity bounded boolean particle swarm optimization for
improved feature selection in liver and kidney disease diagnosis, Expert Systems with Applications, 56 (2016)
28–47.
[6] E. Gundogan and B. Kaya, A link prediction approach for drug recommendation in disease-drug bipartite network,
Artific. Intell. Data Process. Symp. 2017 pp. 1–4.
[7] M.J. Jahid and J. Ruan, An ensemble approach for drug side effect prediction, bioinformatics and biomedicine
(BIBM), 2013 IEEE Int. Conf. IEEE (2013) 440–445.
[8] M.H.M. Lau and J.W. Tenney, Evaluation of drug-disease interactions and their association with unplanned
hospital readmission utilizing STOPP version 2 criteria, Geriatrics 2 (2017) 33.
[9] U. Maulik and D. Chakraborty, Fuzzy preference based feature selection and semisupervised SVM for cancer
classification, IEEE transactions on nanobioscience, 13(2) (2014) 152–160.
[10] H. Moghadam, M. Rahgozar and S. Gharaghani, Scoring multiple features to predict drug disease associations
using information fusion and aggregation, SAR and QSAR in Environmental Research, 27(8) (2016) 609–628.
[11] V. Mudaliar, P. Savaridaasan and S. Garg, Disease prediction and drug recommendation android application using
data mining (virtual doctor), Int. J. Recent Technol. Engin. 8(3) (2019) 6996–7001.
[12] L. Peska, K. Buza and J. Koller, Drug-target interaction prediction: A bayesian ranking approach, Computer
methods and programs in biomedicine, 152 (2017) 15–21.
[13] A. Sharma and R. Rani, BE-DTI’: Ensemble framework for drug target interaction prediction using dimensionality
reduction and active learning, Computer Methods and Programs in Biomedicine, 165 (2018) 151–162.
[14] M.K. Sohrabi and A. Tajik, Multi-objective feature selection for warfarin dose prediction, Computational biology
and chemistry, 69 (2017) 126–133.
[15] F. Viegas, L. Rocha, M. Gon¸calves, F. Mour˜ao, G. S´a, T. Salles, G. Andrade and I. Sandin, A genetic programming
approach for feature selection in highly dimensional skewed data, Neurocomputing, 273 (2018) 554–569.
[16] T. Vivekanandan and N.C.S.N. Iyengar, Optimal feature selection using a modified differential evolution algorithm
and its effectiveness for prediction of heart disease, Computers in biology and medicine, 90 (2017) 125–136.
[17] A.P. Wright, A.T. Wright, A.B. McCoy and D.F. Sittig, The use of sequential pattern mining to predict next
prescribed medications, J. Biomed. Inf. 53 (2015) 73–80.
[18] G. Wu, J. Liu and C. Wang, Predicting drug-disease interactions by semi-supervised graph cut algorithm and
three-layer data integration, BMC Med. Genom. 10(5) (2017) 79.
[19] Y. Yang, Z. Ye, Y. Su, Q. Zhao, X. Li and D. Ouyang, Deep learning for in vitro prediction of pharmaceutical
formulations, Actapharmaceuticasinica B 9(1) (2019) 177–185.
[20] L. Zhang, K. Mistry, C.P. Lim and S.C. Neoh, Feature selection using firefly optimization for classification and
regression models, Decision Support Systems, 106 (2018) 64–85.
[21] D. Zhou, L. Miao and Y. He, Position-aware deep multi-task learning for drug–drug interaction extraction, Artific.
Intell. Med. 87 (2018) 1–8.
Volume 12, Special Issue
December 2021
Pages 2137-2151
  • Receive Date: 06 October 2021
  • Revise Date: 02 November 2021
  • Accept Date: 08 December 2021