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- W4386973350 abstract "Organ transplantation is an important treatment for incurable end-stage disease. The survival analysis method is an appropriate or basic technique to find out the influence of such an operation. Objective of this research is to present a methodology for predicting patient’s best outcomes after organ donation. The importance of computer-based medical prediction is growing as the number of medical records grows daily. Additionally, the usage of machine learning techniques is necessary to discern patterns from these massive amounts of data. We propose a multilayer perceptron neural network model which is definite for the prediction of the survival of a patient who undergoes through transplantation procedure. In this study we are using two different datasets for experimentation; heart transplantation supplied by UNOS and liver transplantation supplied by the Stanford LT program. For dimensionality reduction, the Principle Component Analysis (PCA) with ranking is applied. The Association rule mining algorithm generates rules for association and correlation between the attributes. The model separates the data set into training and test sets by extracting the pertinent characteristics. The input dataset is subjected to k-fold cross-validation. We achieved accuracy result of multilayer perceptron neural network model, for heart transplantation dataset is 72.093% and for liver transplantation dataset is 87.5% respectively. The study indicates multilayer perceptron is better for correct outcome of predicting a long survival of organ after transplantation." @default.
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- W4386973350 date "2023-01-01" @default.
- W4386973350 modified "2023-09-29" @default.
- W4386973350 title "Machine Learning Prediction Models to Predict Long-Term Survival After Heart and Liver Transplantation" @default.
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- W4386973350 doi "https://doi.org/10.1007/978-981-99-3758-5_51" @default.
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