Abstract
The abundance of medical evidence in health institutions necessitates the creation of effective data collection methods for extracting valuable information. For several years, scholars focused on the use of computational techniques and data processing techniques in order to enhance the study of broad historical datasets. There is a deficiency to investigate the collected data of health disease in the data sources such as COVID-19, Chronic Kidney, Epileptic Seizure, Parkinson, Hard diseases, Hepatitis, Breast Cancer and Diabetes, where millions of people are killed in the world by these diseases. This research aims to investigate the neural network algorithms for different types of medical diseases in order to select the best type of neural network suitable for each disease. The data mining process has been applied to investigate the mentioned medical disease datasets. The related works and literature review of machine learning in the medical domain were studied in the initial stage of this research. Then, the experiments behind the initial stage have been designed with six neural network algorithm styles which are Multiple, Radial Based Function Network (RBFN), Dynamic, Quick and Prune algorithms. The extracted results for each algorithm have been analyzed and compared with each other to select the perfect neural network algorithm for each disease. T-test statistical significance test has been applied as one of the investigation strategies for the NN optimal selection. Our findings highlighted the strong side of the Multiple NN algorithm in terms of training and testing phases in the medical domain.
Highlights
The number of records in health databases is so huge thanks to technology which has made it possible to securely and effectively store and retrieve this large volume of data
We firmly agree that the relationship between patient and doctor will be the fundamental cornerstone of treatment for many patients and that new developments into machine learning will contribute to this relationship
We highlighted the quality of illness prediction models by utilising Quick, Multiple, Dynamic, and Radial Based Function Network (RBFN) neural network algorithms, as well as prune neural network algorithm
Summary
The number of records in health databases is so huge thanks to technology which has made it possible to securely and effectively store and retrieve this large volume of data. The process of creating meaningful patterns or evidence from medical data sites is medical diagnosis [1]. The extract from these medical datasets helps the physician to diagnose disease in the early stages. We perform studies in this paper on a number of medical datasets using a variety of familiar prediction algorithms. Materials containing the missing values, contours and noise are well known, and few papers analyze the effect of pre-processing to the best knowledge of the author [3]
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