Soft Optimization of Treatment Methods for a Group of Neurological Diseases Using Data Mining
DOI:
https://doi.org/10.14571/brajets.v17.n1.302-316Palavras-chave:
soft optimization, treatment methods, data mining, prediction of neurological diseasesResumo
The growing healthcare industry is generating large amounts of useful data on patient demographics, treatment plans, payment, and insurance coverage, attracting the attention of clinicians and scientists alike. The purpose of this research is to predict diseases of the brain and nerves using data mining techniques. In this research, after data preparation, disease prediction has been attempted using large matrix methods and data mining techniques. By examining the new vector, we can find out which of the diseases in the matrix will be closer to this new disease with new symptoms using the rows of the matrix. The conducted research is one of descriptive-analytical and applied studies. In this research, we used different meters such as Manhattan, cosine similarity, Pearson, Minkowski and K nearest neighbor and implemented a program to predict neurological diseases using Python software. In the algorithm implemented by Python software, the doctor enters the symptoms of the patient and the program output of each meter shows three diseases close to the input symptoms and finally all the meters are compared and each time the meter is executed, which has a weaker result is determined. The advantages of each of these meters are explained below.Referências
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Direitos de Autor (c) 2024 Shabnam Zarghami, Gholam Hassan Shirdel , Mojtaba Ghanbari
Este trabalho encontra-se publicado com a Licença Internacional Creative Commons Atribuição 4.0.
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