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Detection of Drug Abuse Using Rough Set and Neural Network-Based Elevated Mathematical Predictive Modelling

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Abstract

In the smart world scenario, the application of the Internet of Things is manifold as its utilization can be observed in health care services, home, and office automation, grid systems etc. This was evident during the current pandemic situation when detectors were developed to identify the infected and corresponding medication dosage to develop immunity in the healthcare delivery system. It helped the researchers to assess the required medical treatment with reliable and available data. Similarly, the impact of drugs is observed with the proposed predictive model and its pattern sequences, which act as a lifesaver by timely and accurate medication provided to the patients. This outcome is possible with enhancement and optimization in merging machine learning techniques. The vital objective of this study is to define an optimized framework that integrates rough set-based predictive models and adaptive multi-layer perceptron neural networks that utilize resilient backpropagation algorithms. A minimal set of attributes has been extracted by the Rough Set approach and classified using error optimized backpropagation neural network. model. The performance of the proposed model is evaluated via simulation analysis and compared with conventional backpropagation, resilient backpropagation, modified globally convergent version of resilient backpropagation, and support vector machine algorithms for better validation. The proposed optimized predictive models are found to reduce the cross-entropy values for drug consumption corresponding to alcohol, amphetamines, amyl nitrite, benzodiazepine, caffeine, chocolate, cocaine, heroin, mushrooms, and nicotine whose values are in the range between 0.0101 and 0.0039. Obtained results show that the rough set approach integrated adaptive multi-layer perceptron neural network with resilient backpropagation algorithm gives better performances than conventional methods.

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Availability of data and materials

Data used in the preparation of this research paper can be referred to in the below link; https://archive.ics.uci.edu/ml/datasets/Drug+consumption+%28quantified%29.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Government College of Engineering, Bargur, Tamilnadu, India

    S. Selvi

  2. Government College of Engineering, Bargur, Tamilnadu, India

    M. Chandrasekaran

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SS conceived the concept, conducted experiments, developed the algorithm, and contributed to the preparation of this manuscript. MC participated in the design and helped to draft the manuscript.

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Selvi, S., Chandrasekaran, M. Detection of Drug Abuse Using Rough Set and Neural Network-Based Elevated Mathematical Predictive Modelling. Neural Process Lett 55, 2633–2660 (2023). https://doi.org/10.1007/s11063-022-11086-z

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