Abstract
Globally, chronic liver disease continues to be a major health concern that requires precise predictive models for prompt detection and treatment. Using the Indian Liver Patient Dataset (ILPD) from the University of California at Irvine's UCI Machine Learning Repository, a number of machine learning algorithms are investigated in this study. The main focus of our research is this dataset, which includes the medical records of 583 patients, 416 of whom have been diagnosed with liver disease and 167 of whom have not. There are several aspects to this work, including feature extraction and dimensionality reduction methods like Linear Discriminant Analysis (LDA), Factor Analysis (FA), t-distributed Stochastic Neighbour Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP). The purpose of the study is to investigate how well these approaches work for converting high-dimensional datasets and improving prediction accuracy. To assess the prediction ability of the improved models, a number of classification methods were used, such as Multi-layer Perceptron, Random Forest, K-nearest neighbours, and Logistic Regression. Remarkably, the improved models performed admirably, with Random Forest having the highest accuracy of 98.31% in 10-fold cross-validation and 95.79% in train-test split evaluation. Findings offer important new perspectives on the choice and use of customized feature extraction and dimensionality reduction methods, which improve predictive models for patients with chronic liver disease.
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Abstract
The purpose of this study is to determine the efficieny different categorization techniques work with sound samples in order to identify Parkinson's disease. The research explores classification strategies targeted at improving prediction performance in high-dimensional datasets by utilizing the UCI Parkinson dataset, which consists of 80 sound samples with 240 instances from 40 patients and 40 healthy individuals. This study focuses on the effectiveness of various classification strategies transform high-dimensional data and increase prediction accuracy. Key components of the methodology include Principal Component Analysis (PCA), Fourier and wavelet transforms, replication dependency accounting, and temporal feature extraction. KNN and logistic regression worked together to produce performance metrics and accuracy that were mediocre. Great performance was shown by Random Forest, especially when dealing with high-dimensional datasets. Remarkably, MLP achieved exceptional results of 95% in 10-fold cross validation, outperforming all other models in terms of accuracy, precision, recall, and F1-score.
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