Intelligent Diagnostic Model for Early Malaria Symptoms

One of the most significant worldwide health concerns in low-middle-income nations over the past few decades is Malaria, especially in Kenya. In Kenya, seventy per cent of people reside in areas where malaria is widespread, and most of them face obstacles getting access to medical care because of social culture, distance, and lack of money. Malaria transmission is high, particularly in Kenya’s remote areas, despite a plethora of scientific efforts to combat the disease. This study aims to design and develop an intelligent malaria diagnosis model for early symptom detection using an Adaptive Neuro-fuzzy-Inference System with a 2000 dataset extracted from Six Types of Patient Data Inputs to optimize the model performance. The result achieved was 98.3% accuracy, which contrasted with the pertinent cutting-edge finding to illustrate the benefits of the suggested approach. The main contributions of this study are a combined Six Types of Patient Data Inputs, including Demographic, Symptoms, Blood pressure, Heartbeats, Height, and Weight, using fuzzy Systems techniques to detect early malaria symptoms accurately. The combined patient data input used for evaluation is demonstrated in the results, and the technique can identify different forms of malaria and has the best outcome when compared to relevant findings from the existing studies.