A novel approach for human diseases prediction using nature inspired computing & machine learning approach

Globally, patients with diabetes, diabetic retinopathy, cancer, and heart disease are growing rapidly in developed and developing countries. As a result of these ailments, the rate of human mortality and vision loss has risen dramatically. The design and development of computer-based prediction systems may facilitate the appropriate treatment of these four illnesses by medical professionals. For the design of an efficient and fast prediction (or classification) system, it is necessary to use efficient feature selection techniques to reduce the complexity of the feature space. If there are n features, then there is a possibility that 2 n subsets of features can be created, and testing all of these subsets of selected features would require a significant amount of time. The suggested technique is to investigate the application of ant-lion based optimization to choose a subset of features. The chosen characteristics are used to train and evaluate four classifiers (and their ensemble) based on machine learning. The study used over three public benchmark datasets and one privately composed dataset, each one was disease-specific. The performance of the recommended strategy was evaluated using five performance assessment measures. This adjustment significantly improves the outcome. The strategy may decrease the initial feature set by up to 50% without impacting performance (in terms of accuracy). We can get maximum accuracies of 84.44% for the heart disease dataset, 79.99% for the diabetes dataset, 98.52% for the diabetic retinopathy dataset, and 97.18% for the skin cancer dataset. This empirical research will help doctors and all people make better decisions by giving them a second opinion.