Probabilistic Model-Based Malaria Disease Recognition System
In this paper, we present a probabilistic-based method to predict malaria disease at an early stage. Malaria is a very dangerous disease that creates a lot of health problems. Therefore, there is a need for a system that helps us to recognize this disease at early stages through the visual symptoms...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6633806 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850228211557859328 |
|---|---|
| author | Rahila Parveen Wei Song Baozhi Qiu Mairaj Nabi Bhatti Tallal Hassan Ziyi Liu |
| author_facet | Rahila Parveen Wei Song Baozhi Qiu Mairaj Nabi Bhatti Tallal Hassan Ziyi Liu |
| author_sort | Rahila Parveen |
| collection | DOAJ |
| description | In this paper, we present a probabilistic-based method to predict malaria disease at an early stage. Malaria is a very dangerous disease that creates a lot of health problems. Therefore, there is a need for a system that helps us to recognize this disease at early stages through the visual symptoms and from the environmental data. In this paper, we proposed a Bayesian network (BN) model to predict the occurrences of malaria disease. The proposed BN model is built on different attributes of the patient’s symptoms and environmental data which are divided into training and testing parts. Our proposed BN model when evaluated on the collected dataset found promising results with an accuracy of 81%. One the other hand, F1 score is also a good evaluation of these probabilistic models because there is a huge variation in class data. The complexity of these models is very high due to the increase of parent nodes in the given influence diagram, and the conditional probability table (CPT) also becomes more complex. |
| format | Article |
| id | doaj-art-229cb8b1499e4aaa943a9dcc48d6f18d |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-229cb8b1499e4aaa943a9dcc48d6f18d2025-08-20T02:04:36ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66338066633806Probabilistic Model-Based Malaria Disease Recognition SystemRahila Parveen0Wei Song1Baozhi Qiu2Mairaj Nabi Bhatti3Tallal Hassan4Ziyi Liu5Henan Academy of Big Data, Zhengzhou University, Henan, Zhengzhou, ChinaHenan Academy of Big Data, Zhengzhou University, Henan, Zhengzhou, ChinaSchool of Information Engineering, Zhengzhou University, Henan, Zhengzhou, ChinaDepartment of Information Technology, Shaheed Benazir Bhutto University, Shaheed Benazirabad, Nawabshah, PakistanSchool of Business, Zhengzhou University, Henan, Zhengzhou, ChinaCollege of Engineering & Science, University of Detroit Mercy, Detroit, USAIn this paper, we present a probabilistic-based method to predict malaria disease at an early stage. Malaria is a very dangerous disease that creates a lot of health problems. Therefore, there is a need for a system that helps us to recognize this disease at early stages through the visual symptoms and from the environmental data. In this paper, we proposed a Bayesian network (BN) model to predict the occurrences of malaria disease. The proposed BN model is built on different attributes of the patient’s symptoms and environmental data which are divided into training and testing parts. Our proposed BN model when evaluated on the collected dataset found promising results with an accuracy of 81%. One the other hand, F1 score is also a good evaluation of these probabilistic models because there is a huge variation in class data. The complexity of these models is very high due to the increase of parent nodes in the given influence diagram, and the conditional probability table (CPT) also becomes more complex.http://dx.doi.org/10.1155/2021/6633806 |
| spellingShingle | Rahila Parveen Wei Song Baozhi Qiu Mairaj Nabi Bhatti Tallal Hassan Ziyi Liu Probabilistic Model-Based Malaria Disease Recognition System Complexity |
| title | Probabilistic Model-Based Malaria Disease Recognition System |
| title_full | Probabilistic Model-Based Malaria Disease Recognition System |
| title_fullStr | Probabilistic Model-Based Malaria Disease Recognition System |
| title_full_unstemmed | Probabilistic Model-Based Malaria Disease Recognition System |
| title_short | Probabilistic Model-Based Malaria Disease Recognition System |
| title_sort | probabilistic model based malaria disease recognition system |
| url | http://dx.doi.org/10.1155/2021/6633806 |
| work_keys_str_mv | AT rahilaparveen probabilisticmodelbasedmalariadiseaserecognitionsystem AT weisong probabilisticmodelbasedmalariadiseaserecognitionsystem AT baozhiqiu probabilisticmodelbasedmalariadiseaserecognitionsystem AT mairajnabibhatti probabilisticmodelbasedmalariadiseaserecognitionsystem AT tallalhassan probabilisticmodelbasedmalariadiseaserecognitionsystem AT ziyiliu probabilisticmodelbasedmalariadiseaserecognitionsystem |