Anurag Jain

TL;DR: In this paper, the authors have proposed a hybrid decision support system that can assist in the early detection of heart disease based on the clinical parameters of the patient using multivariate imputation by chained equations algorithm to handle the missing values.

TL;DR: Shamiktiwari et al. as mentioned in this paper proposed a decision support system based on the X-ray image to diagnose the presence of the COVID-19 virus in the human body.

TL;DR: In this paper, a speech signal-based hybrid Parkinson's disease diagnosis system for its early diagnosis was proposed, where three feature selection methods such as mutual information gain, extra tree, and genetic algorithm and three classifiers namely naive bayes, k-nearest neighbors, and random forest have been used.

TL;DR: Shamiktiwari et al. as discussed by the authors proposed a ConvNet model trained by Hybrid Constant-Q Transform (HCQT) for heart sound beat classification, which achieved 96% in multi-class classification.

TL;DR: This work has proposed a Parkinson’s disease diagnosis system by analyzing the kinematic features extracted from the handwritten spirals drawn by patients, and the combination of mutual information gain feature selection method with AdaBoost classifiers outperforms with 96.02% accuracy.