A classification framework for drug relapse prediction

This paper proposes a framework for relapse prediction using Artificial Neural Network algorithms among drug addicts at Pusat Rawatan Inabah. The data collected will be mining through Artificial Neural Network algorithms to generate patterns and useful knowledge and then automatically classifying the relapse possibility. This research collaborates with Pusat Rawatan Inabah, which is one of the rehabilitation centers that provide a specific treatment to rehabilitate the drug addicts from addiction. We expect that among the classification data mining algorithms, Artificial Intelligence Neural Network (ANN) is one of the best algorithms to predict relapse among drug addicts. This may help the rehabilitation center to predict relapse individually and the prediction result is hoped to prevent drug addicts from relapse. Keywords : classification; artificial neural network; drug addiction; Inabah rehabilitation.

[1]  Anthony C. Fisher,et al.  The differential diagnosis of vertical strabismus from prism cover test data using an artificially intelligent expert system , 2007, Medical & Biological Engineering & Computing.

[2]  Zairi Ismael Rizman,et al.  HEART ABNORMALITY DETECTION BY USING ARTIFICIAL NEURAL NETWORK , 2018 .

[3]  UTILIZATION OF DATA MINING TECHNIQUES FOR DIAGNOSIS OF DIABETES MELLITUS-A CASE STUDY , 2015 .

[4]  Norhaidah Abu Haris,et al.  A study of open-source data mining tools for forecasting , 2015, IMCOM.

[5]  M. Manimekalai,et al.  Study of Heart Disease Prediction using Data Mining , 2014 .

[6]  Zairi Ismael Rizman,et al.  PREDICTION OF RAINFALL BASED ON WEATHER PARAMETER USING ARTIFICIAL NEURAL NETWORK , 2018 .

[7]  Zairi Ismael Rizman,et al.  Comparison between Cascade Forward and Multi-Layer Perceptron Neural Networks for NARX Functional Electrical Stimulation (FES)-Based Muscle Model , 2017 .

[8]  Y. Shaham,et al.  Recent developments in animal models of drug relapse , 2013, Current Opinion in Neurobiology.

[9]  Sunil K. Sinha,et al.  Artificial Neural Network Prediction Models for Soil Compaction and Permeability , 2008 .

[10]  Illhoi Yoo,et al.  Data Mining in Healthcare and Biomedicine: A Survey of the Literature , 2012, Journal of Medical Systems.

[11]  T. Velmurugan,et al.  A Comparative Analysis on the Evaluation of Classification Algorithms in the Prediction of Students Performance , 2015 .

[12]  Jorge Bernardino,et al.  Comparison of data mining techniques and tools for data classification , 2013, C3S2E '13.

[13]  Abbas Sheikhtaheri,et al.  Developing and Using Expert Systems and Neural Networks in Medicine: A Review on Benefits and Challenges , 2014, Journal of Medical Systems.

[14]  P. Burnap,et al.  A Naïve Bayes Approach to Classifying Topics in Suicide Notes , 2012, Biomedical informatics insights.

[15]  M. Serdar Bascil,et al.  A Study on Hepatitis Disease Diagnosis Using Multilayer Neural Network with Levenberg Marquardt Training Algorithm , 2011, Journal of Medical Systems.

[16]  Azuraliza Abu Bakar,et al.  A Comparative Study for Various Methods of Classification , 2012 .

[17]  K Kasikumar,et al.  Applications of Data Mining Techniques in Healthcare and Prediction of Heart Attacks , 2018, International Journal of Data Mining Techniques and Applications.

[18]  Donna J. Calu,et al.  The reinstatement model of drug relapse: recent neurobiological findings, emerging research topics, and translational research , 2013, Psychopharmacology.

[19]  Mosima Anna Masethe,et al.  Prediction Of Heart Disease Using Classification Algorithms , 2020 .

[20]  Zairi Ismael Rizman,et al.  DETECTION OF ASPHYXIA IN INFANTS USING DEEP LEARNING CONVOLUTIONAL NEURAL NETWORK (CNN) TRAINED ON MEL FREQUENCY CEPSTRUM COEFFICIENT (MFCC) FEATURES EXTRACTED FROM CRY SOUNDS , 2018 .

[21]  K. Lynch,et al.  Alcohol relapse as a function of relapse definition in a clinical sample of adolescents. , 2003, Addictive behaviors.

[22]  M. Serdar Bascil,et al.  A Study on Hepatitis Disease Diagnosis Using Probabilistic Neural Network , 2012, Journal of Medical Systems.