Abstract
In today's world, due to the increase of medical data there is an interest in data preprocessing, classification and prediction of disease risks. Machine learning and Artificial Intelligence indicates that the predictive analysis becomes part of the medical activities especially in the domain of medical death prevention. The proposed work is focused on supervised learning methods and their capability to find hidden patterns in the real historical medical data. The objective is to predict future risk with a certain probability using Multi-layer perceptron (MLP) method. In the proposed work, MLP based on data classification technique is used for accurate classification and risk analysis of medical data. The proposed method is compared with traditional classification methods and the results show that the proposed method is better than the traditional methods.
Highlights
Nowadays, we are often dealing with data that contains many variables, columns, or attributes
The first dataset here we considered is a Wisconsin Breast Cancer (WBC) and it has total nine features and almost 500 samples are for training and 200 were testing samples
The subsequent section discuss about the performance of proposed mechanism with respect to different data sets which are taken from the University of California at Irvine (UCI) repository
Summary
We are often dealing with data that contains many variables, columns, or attributes. Those datasets, which can be huge in terms of variables or rows, are called high-dimensional data. A text document, is another example of high-dimensional data in which every pixel or word can be seen as a feature. Processing, analysing, and organizing high-dimensional data can be difficult for contemporary systems. It is computationally costly, hard to interpret, and especially in the Machine Learning domain, it could lead the curse of dimensionality. The curse of dimensionality is a phenomenon that potentially arises when the data has too many variables and samples. Curse of dimensionality leads to poor predictive performance and high computational time required to train the predictive model [2]
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