scholarly journals Prediction of Brain Stroke Severity Using Machine Learning

2020 ◽  
Vol 34 (6) ◽  
pp. 753-761
Author(s):  
Vamsi Bandi ◽  
Debnath Bhattacharyya ◽  
Divya Midhunchakkravarthy
Keyword(s):  
Machine Learning ◽  
Central Nervous System ◽  
Nervous System ◽  
Medical Information ◽  
Research Work ◽  
Machine Learning Techniques ◽  
World Health ◽  
Stroke Severity ◽  
Learning Techniques ◽  
The Central Nervous System

In recent years strokes are one of the leading causes of death by affecting the central nervous system. Among different types of strokes, ischemic and hemorrhagic majorly damages the central nervous system. According to the World Health Organization (WHO), globally 3% of the population are affected by subarachnoid hemorrhage, 10% with intracerebral hemorrhage, and the majority of 87% with ischemic stroke. In this research work, Machine Learning techniques are applied in identifying, classifying, and predicting the stroke from medical information. The existing research is limited in predicting risk factors pertained to various types of strokes. To address this limitation a Stroke Prediction (SPN) algorithm is proposed by using the improvised random forest in analyzing the levels of risks obtained within the strokes. This research of the Stroke Predictor (SPR) model using machine learning techniques improved the prediction accuracy to 96.97% when compared with the existing models.

A Review of Statistical and Machine Learning Techniques for Microvascular Complications in Type 2 Diabetes

2020 ◽  
Vol 16 ◽  
Author(s):  
Nitigya Sambyal ◽  
Poonam Saini ◽  
Rupali Syal
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Clinical Medicine ◽  
Microvascular Complications ◽  
Descriptive Analysis ◽  
Machine Learning Techniques ◽  
World Health ◽  
Public Health Issue ◽  
Learning Techniques ◽  
Health Organization

Background and Introduction: Diabetes mellitus is a metabolic disorder that has emerged as a serious public health issue worldwide. According to the World Health Organization (WHO), without interventions, the number of diabetic incidences is expected to be at least 629 million by 2045. Uncontrolled diabetes gradually leads to progressive damage to eyes, heart, kidneys, blood vessels and nerves. Method: The paper presents a critical review of existing statistical and Artificial Intelligence (AI) based machine learning techniques with respect to DM complications namely retinopathy, neuropathy and nephropathy. The statistical and machine learning analytic techniques are used to structure the subsequent content review. Result: It has been inferred that statistical analysis can help only in inferential and descriptive analysis whereas, AI based machine learning models can even provide actionable prediction models for faster and accurate diagnose of complications associated with DM. Conclusion: The integration of AI based analytics techniques like machine learning and deep learning in clinical medicine will result in improved disease management through faster disease detection and cost reduction for disease treatment.

scholarly journals Decision Tree: A Machine Learning for Intrusion Detection

2019 ◽  
Vol 8 (6S4) ◽  
pp. 1126-1130
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Detection System ◽  
Research Work ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Legitimate User ◽  
Learning Techniques ◽  
Three Stages

The Intrusion is a major threat to unauthorized data or legal network using the legitimate user identity or any of the back doors and vulnerabilities in the network. IDS mechanisms are developed to detect the intrusions at various levels. The objective of the research work is to improve the Intrusion Detection System performance by applying machine learning techniques based on decision trees for detection and classification of attacks. The methodology adapted will process the datasets in three stages. The experimentation is conducted on KDDCUP99 data sets based on number of features. The Bayesian three modes are analyzed for different sized data sets based upon total number of attacks. The time consumed by the classifier to build the model is analyzed and the accuracy is done.

scholarly journals Pilomyxoid Astrocytoma Occurring in the Third Ventricle

2015 ◽  
Vol 5 ◽  
pp. 41
Author(s):  
Sanghyeon Kim ◽  
Myongjin Kang ◽  
Sunseob Choi ◽  
Dae Cheol Kim
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
World Health Organization ◽  
Third Ventricle ◽  
World Health ◽  
Central Nervous System Tumor ◽  
Pilomyxoid Astrocytoma ◽  
The Third ◽  
The Central Nervous System ◽  
Health Organization

Pilomyxoid astrocytoma (PMA) is a rare central nervous system tumor that has been included in the 2007 World Health Organization Classification of Tumors of the Central Nervous System. Due to its more aggressive behavior, PMA is classified as Grade II neoplasm by the World Health Organization. PMA predominantly affects the hypothalamic/chiasmatic region and occurs in children (mean age of occurrence = 10 months). We report a case of a 24-year-old man who presented with headache, nausea, and vomiting. Brain CT and MRI revealed a mass occupying only the third ventricle. We performed partial resection. Histological findings, including monophasic growth with a myxoid background, and absence of Rosenthal fibers or eosinophilic granular bodies, as well as the strong positivity for glial fibrillary acidic protein were consistent with PMA.

scholarly journals An Advance Resource Reservation Approach in a Cloud Database Environment

2017 ◽  
Author(s):  
Vinicius Da S. Segalin ◽  
Carina F. Dorneles ◽  
Mario A. R. Dantas
Keyword(s):  
Machine Learning ◽  
Research Work ◽  
Resource Reservation ◽  
Machine Learning Techniques ◽  
Advance Reservation ◽  
Running Time ◽  
Knowing That ◽  
Learning Techniques ◽  
Proposed Model ◽  
Cloud Database

AA well-known challenge with long running time queries in database environments is how much time a query will take to execute. This prediction is relevant for several reasons. For instance, by knowing that a query will take longer to execute than desired, one resource reservation mechanism can be performed, which means reserving more resources in order to execute this query in a shorter time in a future request. In this research work, it is presented a proposal in which the use of an advance reservation mechanism in a cloud database environment, considering machine learning techniques, provides resource recommendation. The proposed model is presented, in addition to some experiments that evaluate benefits and the efficiency of this enhanced proposal.

The 2016 World Health Organization classification of tumours of the central nervous system

2018 ◽  
Vol 47 (11-12) ◽  
pp. e187-e200 ◽  
Author(s):  
Chiara Villa ◽  
Catherine Miquel ◽  
Dominic Mosses ◽  
Michèle Bernier ◽  
Anna Luisa Di Stefano
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
World Health Organization ◽  
World Health ◽  
World Health Organization Classification ◽  
The Central Nervous System ◽  
Health Organization

Survey of Breast Cancer Detection Using Machine Learning Techniques in Big Data

2019 ◽  
Vol 21 (3) ◽  
pp. 80-92
Author(s):  
Madhuri Gupta ◽  
Bharat Gupta
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Research Work ◽  
Cancer Recurrence ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Common Disease ◽  
Learning Techniques ◽  
Learning Frameworks ◽  
Processing Engine

Cancer is a disease in which cells in body grow and divide beyond the control. Breast cancer is the second most common disease after lung cancer in women. Incredible advances in health sciences and biotechnology have prompted a huge amount of gene expression and clinical data. Machine learning techniques are improving the prior detection of breast cancer from this data. The research work carried out focuses on the application of machine learning methods, data analytic techniques, tools, and frameworks in the field of breast cancer research with respect to cancer survivability, cancer recurrence, cancer prediction and detection. Some of the widely used machine learning techniques used for detection of breast cancer are support vector machine and artificial neural network. Apache Spark data processing engine is found to be compatible with most of the machine learning frameworks.

Sign in / Sign up

Export Citation Format

Share Document