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Prediction of Malaria using Artificial Neural Network


Rahila Parveen, Akhtar Hussain Jalbani, Mohsin Shaikh, Kashif Hussain Memon, Saima Siraj, Mairaj Nabi and Shamshad Lakho


Vol. 17  No. 12  pp. 79-86


In current era disease are very common but among all Malaria is one of the major one causes of death, whereas every year, malaria is the cause of about three million deaths including one-third of children. Several approaches have been proposed and implemented in which Malaria can only be detected by taking a blood sample of patients in the laboratory. These techniques cause a delay in the start of treatment. Due to which, Death ratio is considerably higher for Malaria disease in the world. The aim of this research is to speed up the process of Malaria diagnosis. An Artificial Neural Network with MPL (Multi Layer Perceptron) is used along with back propagation, back propagation with momentum and resilient propagation rule for the prediction of Malaria. Among all three learning rules, Back propagation gives the more efficient results approximately 85%. In the proposed approach, history and symptoms of patients are considered as an input, system analyses that data and predict the result for the victim as positive or negative for Malaria. This application is useful for those areas where there is no any laboratory facility or where there is no Doctor in such condition the person who able to operate the application by giving only verbal history and physical appearance of the patient.


Malaria detection Artificial Neural Network Multi-Layer Perceptron Efficiency