Feature selection and classification systems for chronic disease prediction: A review
By Divya Jain, V. Singh. Egyptian Informatics Journal, 2018
337 citations recorded by the source corpus.
Abstract Chronic Disease Prediction plays a pivotal role in healthcare informatics. It is crucial to diagnose the disease at an early stage. This paper presents a survey on the utilization of feature selection and classification techniques for the diagnosis and prediction of chronic diseases. Adequate selection of features plays a significant role for enhancing accuracy of classification systems. Dimensionality reduction helps in improving overall performance of machine learning algorithm. The application of classification algorithms on disease datasets yields promising results by developing adaptive, automated and intelligent diagnostic systems for chronic diseases. Parallel classification systems can be used to expedite the process and to enhance the computational efficiency of results. This work presents a comprehensive overview of various feature selection methods and their inherent pros and cons. We then analyze adaptive classification systems and parallel classification systems for chronic disease prediction.
Subfields: Artificial Intelligence, Health Information Management, Information Systems
View Paper: https://www.semanticscholar.org/paper/691cf5579d3867ae05374a8beab8a25b05467a22
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Citations in the corpus
- Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda (2022, methodology): “The other techniques based on AI are stated below (Jain et al. 2018).”
- A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction (2020, methodology): “Most wrapper methods are multivariate; thus, they need extensive computation times to achieve the convergences and can be intractable for large datasets [33, 34].”