Probabilistic Model-Based Malaria Disease Recognition System

Rahila Parveen,Baozhi Qiu,Mairaj Nabi Bhatti,Ziyi Liu,Tallal Hassan,Wei Song,Dr Shahzad Sarfraz

doi:10.1155/2021/6633806

Abstract

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.

Highlights

Is life-threatening disease is spread with the bite of female mosquitoes. ese female mosquitos are responsible for spreading the Plasmodium parasite from one person to another, and these for the most infect the humans in between twilight and dawning time bites
Due to these Plasmodium infection [2] patients, red blood cells are damaged, which leads to this disease. ere are five major categories of parasites by which humans are getting infected with malaria, namely, Plasmodium falciparum, Plasmodium vivax, Plasmodium malariae, Plasmodium ovale, and Plasmodium knowlesi [3]
Many classifiers such as artificial neural network (ANN), support vector machines (SVM), decision tree, and Bayesian network (BN) have been used in medical diagnosis to get acceptable results; complexities of these algorithms are different which depend on data sets used for prediction

Summary

Introduction

Is life-threatening disease is spread with the bite of female mosquitoes. ese female mosquitos are responsible for spreading the Plasmodium parasite from one person to another, and these for the most infect the humans in between twilight and dawning time bites. As per the current study, it is suggested that probabilistic models are fit for learning information for the prediction of malaria disease. By the extracted symptom data and clinical reports, the information will be trained into the proposed framework for the accuracy of malaria disease recognition.

Results

Conclusion