A Probabilistic Neural Network Approach for Classification of Datasets Collected from North Coastal Districts of AP, India Using MatLab

AbstractData mining is an important tool to analyze the data for diseased datasets. The data has been collected from North coastal districts of AP, India during 2011 to 2014 with 504 instances and 56 attributes. The methods like Confusion matrix, ROC, Best validation performance, R value, SOM Topology, Hits, SOM Neighbor Connections, Neighbor weight distances and SOM weight positions were analysed using MatLab version 7.6.0 (R2008a) from the collected dataset from north coastal districts of AP, India in the present study

Subgrouping factors influencing migraine intensity in women: A semi-automatic methodology based on machine learning and information geometry

This is the peer reviewed version of the following article: Pérez-Benito, F.J., Conejero, J.A., Sáez, C., García-Gómez, J.M., Navarro-Pardo, E., Florencio, L.L. and Fernández-de-las-Peñas, C. (2020), Subgrouping Factors Influencing Migraine Intensity in Women: A Semi-automatic Methodology Based on Machine Learning and Information Geometry. Pain Pract, 20: 297-309, which has been published in final form at https://doi.org/10.1111/papr.12854. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.[EN] Background Migraine is a heterogeneous condition with multiple clinical manifestations. Machine learning algorithms permit the identification of population groups, providing analytical advantages over other modeling techniques. Objective The aim of this study was to analyze critical features that permit the differentiation of subgroups of patients with migraine according to the intensity and frequency of attacks by using machine learning algorithms. Methods Sixty-seven women with migraine participated. Clinical features of migraine, related disability (Migraine Disability Assessment Scale), anxiety/depressive levels (Hospital Anxiety and Depression Scale), anxiety state/trait levels (State-Trait Anxiety Inventory), and pressure pain thresholds (PPTs) over the temporalis, neck, second metacarpal, and tibialis anterior were collected. Physical examination included the flexion-rotation test, cervical range of cervical motion, forward head position while sitting and standing, passive accessory intervertebral movements (PAIVMs) with headache reproduction, and joint positioning sense error. Subgrouping was based on machine learning algorithms by using the nearest neighbors algorithm, multisource variability assessment, and random forest model. Results For migraine intensity, group 2 (women with a regular migraine headache intensity score of 7 on an 11-point Numeric Pain Rating Scale [where 0 = no pain and 10 = maximum pain]) were younger and had lower joint positioning sense error in cervical rotation, greater cervical mobility in rotation and flexion, lower flexion-rotation test scores, positive PAIVMs reproducing migraine, normal PPTs over the tibialis anterior, shorter migraine history, and lower cranio-vertebral angles while standing than the remaining migraine intensity subgroups. The most discriminative variable was the flexion-rotation test score of the symptomatic side. For migraine frequency, no model was able to identify differences between groups (ie, patients with episodic or chronic migraine). Conclusions A subgroup of women with migraine who had common migraine intensity was identified with machine learning algorithms.Perez-Benito, FJ.; Conejero, JA.; Sáez Silvestre, C.; Garcia-Gomez, JM.; Navarro-Pardo, E.; Florencio, LL.; Fernández-De-Las-Peñas, C. (2020). Subgrouping factors influencing migraine intensity in women: A semi-automatic methodology based on machine learning and information geometry. Pain Practice. 20(3):297-309. https://doi.org/10.1111/papr.12854S29730920

UNDERSTANDING OF PROTEIN INTERACTION NETWORKS BETWEEN NORMAL AND CANCER CONDITIONS USING SELECTED ATTRIBUTES

ABSTRACT Finding of expression and networks between cancer causing proteins are the principle and basic level of understanding in control of cancer. The present network analysis was provided an understanding of protein networks for selected proteins/genes AKT1, ALK, BRAF, EGFR, HER2, KRAS, MEK1, MET, NRAS, PIK3CA, RET, ROS1 AR, ER, FGFR1, FGFR2, PIK3CA, PR and PTEN oncogenes. The study showed that two genes, AKT1 and PIK3CA are largely associated with both lung and breast cancers