A supervised machine learning approach to predict vulnerability to drug addiction
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.
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| Idioma: | English |
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Brac University
2020
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| Acesso em linha: | http://hdl.handle.net/10361/13779 |
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10361-137792022-01-26T10:04:57Z A supervised machine learning approach to predict vulnerability to drug addiction Faisal, Fahim Shahriar, Arif Mahmud, Sohan Uddin Shuvo, Rakibul Alam Chakrabarty, Amitabha Department of Computer Science and Engineering, Brac University Primary data mRMR Deep beilief nerwork Reliability Vulnerability to addiction Neural network Random forest Neural networks (Computer science) Machine learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019. Cataloged from PDF version of thesis. Includes bibliographical references (pages 72-74). There are signi cant amount of di erences between an addicted and non-addicted person on their social and familial behavior. In our thesis we tried to nd out the characteristics of a person related to his social and familial life and also health issues that can prove his vulnerability to drug addiction. The research was held on the context of the people of Dhaka, Bangladesh and on an age group of 15 to 40 years. A primary data set was constructed which include 498 samples. For constructing the questionnaire Addiction Severity Index and WHO's Assist Scale were followed along with the help of psychologists and specialists on drug addiction. For addicted person's data we reached some rehabilitation center of Dhaka and for non-addicted person's data we communicated di erent aged group people of di erent colleges and universities. 498 samples where one sample consisted of 60 features were trained and tested by supervised machine learning approach. Reliability of the data set was validated by Cronbach's Alpha Nominal Test. 10 algorithms were incorporated including Neural Network, Deep Belief Network, Random Forest, XGBooster etc. and their results were compared. Among the algorithms, XGB came up with the highest number of accuracy of 95.20% and KNN delivered the least which is 88.97%. In order to select important features mRMR, Chi-square, Principle Component Analysis techniques were used. From feature selection we got the key features of an addicted person's behavior that were in uential for their drug abuse. This will help people to understand if a person is going to be vulnerable to addiction or not based on their health issues and social and familial behavior. Fahim Faisal Arif Shahriar Sohan Uddin Mahmud Rakibul Alam Shuvo B. Computer Science 2020-02-18T05:43:09Z 2020-02-18T05:43:09Z 2019 2019-08 Thesis ID 15201001 ID 15201002 ID 15201006 ID 15201025 http://hdl.handle.net/10361/13779 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 83 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Primary data mRMR Deep beilief nerwork Reliability Vulnerability to addiction Neural network Random forest Neural networks (Computer science) Machine learning |
| spellingShingle |
Primary data mRMR Deep beilief nerwork Reliability Vulnerability to addiction Neural network Random forest Neural networks (Computer science) Machine learning Faisal, Fahim Shahriar, Arif Mahmud, Sohan Uddin Shuvo, Rakibul Alam A supervised machine learning approach to predict vulnerability to drug addiction |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019. |
| author2 |
Chakrabarty, Amitabha |
| author_facet |
Chakrabarty, Amitabha Faisal, Fahim Shahriar, Arif Mahmud, Sohan Uddin Shuvo, Rakibul Alam |
| format |
Thesis |
| author |
Faisal, Fahim Shahriar, Arif Mahmud, Sohan Uddin Shuvo, Rakibul Alam |
| author_sort |
Faisal, Fahim |
| title |
A supervised machine learning approach to predict vulnerability to drug addiction |
| title_short |
A supervised machine learning approach to predict vulnerability to drug addiction |
| title_full |
A supervised machine learning approach to predict vulnerability to drug addiction |
| title_fullStr |
A supervised machine learning approach to predict vulnerability to drug addiction |
| title_full_unstemmed |
A supervised machine learning approach to predict vulnerability to drug addiction |
| title_sort |
supervised machine learning approach to predict vulnerability to drug addiction |
| publisher |
Brac University |
| publishDate |
2020 |
| url |
http://hdl.handle.net/10361/13779 |
| work_keys_str_mv |
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| _version_ |
1814307059398606848 |