HENDRAWANTO, LAURENTIUS KEVIN (2019) DIAGNOSIS OF HUMAN DISEASE USING NAIVE BAYES ALGORITHM. Other thesis, UNIKA SOEGIJAPRANATA SEMARANG.
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Abstract
Human Disease prediction is hard to do because there is a lot of Disease that has the same symptoms. For example, masuk angin, usus buntu, aids and batu ginjal have the same mual symptoms. This is just a small case. In real data, there is more than that. Some disease has its symptoms, some of that share several of their symptoms, and there is a disease that shares all of their symptoms with others. This random case is the problem why a simple query can not get the job done. For making the disease prediction system. It needs an algorithm that can predict the disease using the available data. Naive Bayes is used in this case. Naive Bayes can calculate the inputted data, then make a classification based on the available data. The result is a score so then the system can give prediction to the user. In Naive Bayes, all the disease will be calculated, not just a disease that has inputted symptoms from the user. With that, we can sort the result from the highest to the lowest score. A system that can predict patient disease is the goal of this research. User needs to enter every symptom that happens to their body and system will give a prediction maximum 3 start with the highest score to the lowest score of the 3 highest score. With that, the patient or doctor can decided on the correct one based on doctor experience. The system is used to make the diagnosis process faster than before. Keyword: Naive Bayes classifier, diagnosis, Django framework
| Item Type: | Thesis (Other) |
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| Subjects: | 000 Computer Science, Information and General Works > 005 Computer programming, programs & data > Information Systems 600 Technology (Applied sciences) > 610 Medicine and health > Healthy > Prevention of Disease |
| Divisions: | Faculty of Computer Science > Department of Informatics Engineering |
| Depositing User: | Mr Lucius Oentoeng |
| Date Deposited: | 22 Nov 2019 01:13 |
| Last Modified: | 09 Oct 2020 07:22 |
| URI: | http://repository.unika.ac.id/id/eprint/20035 |
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