A Healthcare Data Analysis Approach for Breast Cancer Gene expression

Healthcare data analysis basically deals with recent and past historical raw data, conducting experimentation to predict future trends, to extend its outreach, to detect disease and even manage the spread of disease. It is strongly predicted that gene expression data will be used to help in the development of better cancer detection and classification models. Gene expression data, on the other hand, is dimensionally high, has a small sample size in comparison to the data dimension, and it is also noisy by nature. As a result, rather than using all genes to classify gene expression data, it would be wiser to use a subset of genes. Analysis of these data helps a lot in patient care, such as disease prediction in the early stage, faster recovery, preventive measures, more person-based treatment, and efficient decision-making. Data is collected from various health information systems to analyze healthcare data. These data are too complex for traditional approaches to analyze. These datasets are analyzed using efficient machine learning approaches. In this research study, neural network and non-neural network based data classification algorithms are used for the analysis of breast cancer datasets.