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 syst...

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Vydáno v:	Multimedia tools and applications Ročník 83; číslo 6; s. 17773 - 17809
Hlavní autoři:	MunishKhanna, Singh, Law Kumar, Garg, Hitendra
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.02.2024 Springer Nature B.V
Témata:	Cancer Cardiovascular disease Computer Communication Networks Computer Science Data Structures and Information Theory Datasets Developing countries Diabetes Diabetic retinopathy Heart diseases LDCs Machine learning Multimedia Information Systems Performance assessment Performance evaluation Skin cancer Special Purpose and Application-Based Systems Track 2: Medical Applications of Multimedia Soft-computing Human disease prediction Feature selection Ant-lion algorithm Machine learning
ISSN:	1573-7721, 1380-7501, 1573-7721
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	1573-7721 1380-7501 1573-7721
DOI:	10.1007/s11042-023-16236-6