Early detection of thyroid disease using feature selection and hybrid machine learning approach
DOI:
https://doi.org/10.56294/hl2024.192Keywords:
Cervical disease, Computer-Aided Diagnostic (CAD)Systems, Machine Learning (ML), Decision Tree (DT)Abstract
In today's environment, thyroid disorders are quite widespread and widely dispersed. They frequently result in serious physical and mental suffering. It interferes with the thyroid gland's ability to operate, which causes the thyroid to secrete too much hormone. The thyroid organs are ground up by the hormones produced when the body enters auto-safe mode in this illness. Avoiding this condition is crucial because it has irreversible effects on the body. Since this disorder is extremely difficult to cure once it reaches its final stage, preventing it from occurring needs some awareness of its development. The ontological challenges and disparate data standards that are employed in Medical Data Analysis (MDA) and system-assisted healthcare management are well-known in the healthcare industry. Rapid technological breakthroughs have drawn researchers to the health sector to create accurate, dependable, and reasonably priced medical (DSS) decision support systems (MDSS). Therefore, there is continuous research being done to construct an efficient and practically applicable MFFN+MLP-based DSS for medical data (MD) processing and knowledge discovery (KD). Using computerised intelligent medical decision support systems offers a practical way to help medical professionals diagnose patients quickly and correctly. Before a practical medical diagnosis system can be created and implemented, a number of problems must be addressed and handled, including how to make decisions when faced with ambiguity and imprecision.
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