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Hepatitis is among the deadliest diseases on the planet. Machine learning approaches can contribute toward diagnosing hepatitis disease based on a few characteristics. On the UCI dataset, authors assessed distinct classifiers' performance in order to develop a systematic strategy for hepatitis disease diagnosis. The classifiers used are support vector machine, logistic regression (LR), K-nearest neighbor, and random forest. The classifiers were employed without class balancing and in conjunction with class balancing using SMOTE strategy. Both studies, classification without class balancing and with class balancing, were compared in terms of different performance parameters. After adopting class balancing, the efficiency of classifiers improved significantly. LR with SMOTE provided the highest level of accuracy (93.18%).

Machine learning is used in the health care sector due to its ability to make predictions. Nowadays major cause of death in women is due to breast cancer. In this paper, a machine learning-based framework for the diagnosis of breast cancer has been proposed. The authors have used different feature selection methods on Breast Cancer Wisconsin (Diagnostic) dataset i.e. Chi-square, Pearson correlation between features and Feature importance. The competency of the feature selection methods has been analyzed using different machine learning classifiers on different performance parameters like accuracy, sensitivity, specificity, precision, and F-measure. Random Forest (RF), Extra Tree Classifier (ETC), and Logistic Regression (LR) machine learning classifiers have been used by the authors. Results reveal that FI (Feature Importance) is the preeminent feature selection method among all others used when applied with different classifiers. Results also show that the ETC machine learning classifier gives the best accuracy result in comparison with RF and LR classifiers.

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