Optimizing Liver Disease Prediction using SMOTE Integrated Supervised Learning Model
Keywords:
Synthetic Minority Over-sampling Technique, Machine learning¸ Data Balancing, Liver Diseases.Abstract
Liver disease is a major worldwide health issue that affects millions of people. Prompt and precise diagnosis is crucial for efficient disease control and improved patient results. Machine learning (ML) methods have demonstrated significant potential in forecasting a range of medical disorders, including liver illnesses. Nevertheless, the efficacy of machine learning models is greatly dependent on the calibre and volume of the training data. Regrettably, numerous datasets are afflicted with class imbalance, wherein specific classes, such as diseased and non-diseased individuals, are not evenly distributed. Addressing this imbalance is critical to better the reliability of liver disease prediction using ML models, since it might result in biased predictions and reduced model accuracy. Thus, the objective of this project is to address the issue of class imbalance by utilizing sophisticated data balancing techniques. The suggested approach includes preparing the dataset using the Synthetic Minority Over-sampling Technique (SMOTE), which generates synthetic samples for the minority class, resulting in a more balanced dataset. Furthermore, it modifies the cost function of the learning process to consider the imbalance in class distribution, hence enhancing the performance of the model. After obtaining a dataset that is evenly distributed, we proceed to train a machine learning model (namely, logistic regression, support vector classifier, and gradient boosting classifier) with the purpose of predicting liver disease. The efficacy of the proposed model is assessed on a separate test dataset, employing diverse criteria like accuracy, precision, recall, and F1-score. By efficiently addressing class imbalance via data balancing algorithms, this model is anticipated to provide significant assistance to medical professionals in the early and precise diagnosis of liver illnesses, ultimately resulting in enhanced patient care and outcomes.
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