Journal: Brain

Article Title: Subthalamic nucleus activity dynamics and limb movement prediction in Parkinson’s disease

doi: 10.1093/brain/awz417

Figure Lengend Snippet: Experimental setup and analysis pipeline. ( A ) Deep brain electrode schematic. ( B ) The directional DBS lead (Boston Scientific). Contacts are distributed along four levels. On levels two and three, there are three segmented contacts (level two: contacts 2/3/4; level three: contacts 5/6/7). ( C ) The fixed-limb voluntary movement task; upper and lower limb movements performed in separate blocks, with each block preceded with an instruction describing the limb to be moved after hearing the imperative auditory cue. ( D ) Random-limb voluntary movement task; upper and lower limb movements randomly instructed within the same experimental block. Here the auditory cue prior to each trial also describes the limb to be moved. ( E ) Flow chart summarizing analysis pipeline. LFP signals are used to predict the limb moved using the naïve Bayes technique. Acc. = acceleration; Ant = anterior; au = arbitrary units; Lat = lateral; Med = medial; Post = posterior.

Article Snippet: The Gaussian Naïve Bayes classifier MATLAB implementation was used to differentiate between the upper and lower limb movements ( Friedman et al. , 1997 ; ; ).

Techniques: Blocking Assay

Journal: Heliyon

Article Title: Exploring the dominant features and data-driven detection of polycystic ovary syndrome through modified stacking ensemble machine learning technique

doi: 10.1016/j.heliyon.2023.e14518

Figure Lengend Snippet: A summary of traditional machine learning classifiers used for different healthcare predictive studies.

Article Snippet: Another work in this domain had been proposed by Agrawal et al. , where the top 30 features from the data were determined using the Chi-square technique, and the underlying state of PCOS was predicted using Random Forest, SVM, Logistic Regression, Gaussian Naive Bayes, and K Neighbors utilizing this reduced feature vector.

Techniques: Plasmid Preparation, Biomarker Discovery

Journal: Heliyon

Article Title: Exploring the dominant features and data-driven detection of polycystic ovary syndrome through modified stacking ensemble machine learning technique

doi: 10.1016/j.heliyon.2023.e14518

Figure Lengend Snippet: F1-Score Comparison of ML models using different set of features.

Article Snippet: Another work in this domain had been proposed by Agrawal et al. , where the top 30 features from the data were determined using the Chi-square technique, and the underlying state of PCOS was predicted using Random Forest, SVM, Logistic Regression, Gaussian Naive Bayes, and K Neighbors utilizing this reduced feature vector.

Techniques: Comparison