Metaheuristics Algorithms for Complex Disease Prediction

Machine learning and metaheuristics algorithms are used to improve the performance of sensor technologies, devices, WSN, and IoT by picking relevant characteristics from raw data to improve device performance or lengthen sensor network lifetime. Metaheuristics repeat evaluation, transition and determination operators till the search procedure converges or satisfies the specified stopping condition. They are particularly beneficial for interpreting protein data and infectious illnesses like Ebola and SARS. The suggested ACS may be used for the prediction of chronic disorders, such as Alzheimer’s Disease, Chronic kidney Disease, Diabetes Mellitus, Hypertension, Ischemic Heart Disease, Gout, Parkinson’s disease, Hepatitis, etc. Data mining algorithms based on metaheuristics may be used to investigate interrelationships between symptoms and diseases, as well as between the cause and effect of human behaviors and the development of disease. Particle swarm optimization (PSO) is a similar approach to GA and ACO that has shown promising results in resolving classification problems and evaluating healthcare data. PSO is also being studied as a classification algorithm for detecting breast cancer. This study used statistical techniques to select relevant features and PSO to divide the population into those with and those without breast cancer. PSO was used to identify the optimal pathophysiological parameter weights for a diagnosis system and then implemented it on a field programmable gate array (FPGA). Recent research has used metaheuristics to overcome healthcare optimization problems, such as the segmentation of magnetic resonance imaging, computed tomography pictures, and images from other sources. Metaheuristics for the study of large amounts of healthcare data is one of the future trends in research, along with the improvement of data storage systems, pre-processing of data extracted and data analysis.