Detecting Adverse Drug Reaction (ADR) with data mining and predicting its intensity with machine learning
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.
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| বিন্যাস: | গবেষণাপত্র |
| ভাষা: | English |
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BRAC University
2018
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| অনলাইন ব্যবহার করুন: | http://hdl.handle.net/10361/10965 |
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10361-109652022-01-26T10:20:02Z Detecting Adverse Drug Reaction (ADR) with data mining and predicting its intensity with machine learning Hussain, Nadib Islam, Tanvir Apu, Rafik Un Nabi Chakrabarty, Amitabha Department of Computer Science and Engineering, BRAC University Adverse Drug Reaction Healthcare Medical diagnosis Random forest Support vector machine Drug-symptom association Data mining Machine learning Drugs -- Side effects. This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. Cataloged from PDF version of thesis. Includes bibliographical references (pages 33-34). Adverse Drug Reaction (ADR) is one of the many uncertainties which are considered as a fatal threat in the field of pharmacy and medical diagnosis. Utmost care is taken to test a new drug thoroughly before it is introduced and made available to the public; but these pre-clinical trials are not enough on their own to ensure safety. Many ADRs are discovered in the later stages of consumption which could not be found out during the pre-clinical trials. The increasing concern to the ADRs has motivated the development of statistical, data mining and machine leaning methods to detect the Adverse Drug Reactions. With the availability of electronic health Records (EHRs) it has become possible to detect ADRs with the mentioned technologies. In this work, we have proposed a hybrid model of data mining and machine learning to identify different Adverse Reactions and predict the intensity of the final outcome. We have used the Proportionality Reporting Ratio (PRR) along with the precision point estimator test called the Chi-Square test to mine the different associations between drug and symptoms called the drug-ADR association. This output from the data mining technique is used as an input to the machine learning algorithms such as Random Forest and Support Vector Machine (SVM) to predict the intensity of the final outcome of ADR, depending on a patient’s demographic data such as gender, weight, age, etc. We have performed the analysis on a total count of 88000 data taken from the publicly available dataset of FDA and achieved an accuracy of 91% to predict ‘death’ as the final outcome from an ADR. Nadib Hussain Tanvir Islam Rafik Un Nabi Apu B. Computer Science and Engineering 2018-12-04T09:47:37Z 2018-12-04T09:47:37Z 2018 2018-07 Thesis ID 15101080 ID 15101113 ID 13101054 http://hdl.handle.net/10361/10965 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. 34 pages application/pdf BRAC University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Adverse Drug Reaction Healthcare Medical diagnosis Random forest Support vector machine Drug-symptom association Data mining Machine learning Drugs -- Side effects. |
| spellingShingle |
Adverse Drug Reaction Healthcare Medical diagnosis Random forest Support vector machine Drug-symptom association Data mining Machine learning Drugs -- Side effects. Hussain, Nadib Islam, Tanvir Apu, Rafik Un Nabi Detecting Adverse Drug Reaction (ADR) with data mining and predicting its intensity with machine learning |
| description |
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. |
| author2 |
Chakrabarty, Amitabha |
| author_facet |
Chakrabarty, Amitabha Hussain, Nadib Islam, Tanvir Apu, Rafik Un Nabi |
| format |
Thesis |
| author |
Hussain, Nadib Islam, Tanvir Apu, Rafik Un Nabi |
| author_sort |
Hussain, Nadib |
| title |
Detecting Adverse Drug Reaction (ADR) with data mining and predicting its intensity with machine learning |
| title_short |
Detecting Adverse Drug Reaction (ADR) with data mining and predicting its intensity with machine learning |
| title_full |
Detecting Adverse Drug Reaction (ADR) with data mining and predicting its intensity with machine learning |
| title_fullStr |
Detecting Adverse Drug Reaction (ADR) with data mining and predicting its intensity with machine learning |
| title_full_unstemmed |
Detecting Adverse Drug Reaction (ADR) with data mining and predicting its intensity with machine learning |
| title_sort |
detecting adverse drug reaction (adr) with data mining and predicting its intensity with machine learning |
| publisher |
BRAC University |
| publishDate |
2018 |
| url |
http://hdl.handle.net/10361/10965 |
| work_keys_str_mv |
AT hussainnadib detectingadversedrugreactionadrwithdataminingandpredictingitsintensitywithmachinelearning AT islamtanvir detectingadversedrugreactionadrwithdataminingandpredictingitsintensitywithmachinelearning AT apurafikunnabi detectingadversedrugreactionadrwithdataminingandpredictingitsintensitywithmachinelearning |
| _version_ |
1814309105316134912 |