Abstract
Background Even in today's environment, when there is a plethora of information accessible, it may be difficult to make appropriate choices for one's well-being. Data mining, machine learning, and computational statistics are among the most popular arenas of training today, and they are all aimed at secondary empowered person in making good decisions that will maximize the outcome of whatever working area they are involved with. Because the degree of rise in the number of patient roles is directly related to the rate of people growth and lifestyle variations, the healthcare sector has a significant need for data processing services. When it comes to cancer, the prognosis is an expression that relates to the possibility of the patient surviving in general, but it may also be used to describe the severity of the sickness as it will present itself in the patient's future timeline. Methodology. The proposed technique consists of three stages: input data acquisition, preprocessing, and classification. Data acquisition consists of input raw data which is followed by preprocessing to eliminate the missed data and the classification is carried out using ensemble classifier to analyze the stages of cancer. This study explored the combined influence of the prominent labels in conjunction with one another utilizing the multilabel classifier approach, which is successful. Finally, an ensemble classifier model has been constructed and experimentally validated to increase the accuracy of the classifier model, which has been previously shown. The entire performance of the recommended and tested models demonstrates a steady development of 2% to 6% over the baseline presentation on the baseline performance. Results Providing a good contribution to the general health welfare of noncommercial potential workers in the healthcare sector is an opportunity provided by this recommended job outcome. It is anticipated that alternative solutions to these constraints, as well as automation of the whole process flow of all five phases, will be the key focus of the work to be carried out shortly. Predicting health status of employee in industry or information trends is made easier by these data patterns. The proposed classifier achieves the accuracy rate of 93.265%.
Highlights
Identifying and extracting hidden patterns from huge volumes of data is a complex operation that needs data mining to be completed successfully
Single-level and one-label predictions are achievable in the medical literature, but the goal of this study is to examine multilevel models and classifier ensembles utilizing datasets from the University of California, Irvine Machine Learning (UCI Machine Learning)
E following is a list of the results that are addressed in chronological order: Results of all cancer types in an increasing range of samples from 1000 to 30000 using the ensemble classifier are expanding
Summary
Identifying and extracting hidden patterns from huge volumes of data is a complex operation that needs data mining to be completed successfully. E primary goal of this thesis is to use data mining techniques to construct an effective prognostic prediction model for medical datasets To conduct this suggested research, the conventional classical CRISP-DM process, as shown, has been changed. Because the degree of rise in the number of patient roles is directly related to the rate of people growth and lifestyle variations, the healthcare sector has a significant need for data processing services. When it comes to cancer, the prognosis is an expression that relates to the possibility of the patient surviving in general, but it may be used to describe the severity of the sickness as it will present itself in the patient’s future timeline. Predicting health status of employee in industry or information trends is made easier by these data patterns. e proposed classifier achieves the accuracy rate of 93.265%
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