scholarly journals A Critique on Heart Diseases Predictive Analytics using Big Data Algorithms

2019 ◽  
Vol 8 (9S2) ◽  
pp. 794-798
Keyword(s):  
Big Data ◽  
Cardiac Disease ◽  
Heart Attack ◽  
Predictive Analytics ◽  
Heart Diseases ◽  
Machine Learning Algorithms ◽  
Risk Level ◽  
Cloud Platform ◽  
Unstructured Information ◽  
Present World

A large volume of both structured and unstructured information is managed by the emerging technology big data. This information is complicated to practice using set records and software techniques. An elite solution is brought in all technologies by using them competently. To improve the prediction of heart diseases earlier and bring more intellectual decisions the big data is potential in healthcare organization. In the present world condition the doctors and experts available are very intricate to forecast the heart diseases. The heart attack has become a remarkable cause of the endless demise worldwide. Heart attack is essential to predict it at an earlier stage to standby the existence of individuals and it is the main source of demise. The primary purpose is to predict the risk level of a person using Big Data algorithms for the cardiac disease. Big Data is primarily designed to provide a national scheme for physicians and patients to login and view Cloud information. Hadoop Map Reduce programming is used to maintain the hospital details. The machine learning algorithms is used to view the precise condition of the patient in its graphical demonstration. Using cloud platform for accessing globally exploitation any browsers in any a part of the globe this application are often enforced.

scholarly journals Predicting Heart Diseases from Large Scale IoT Data Using a Map-Reduce Paradigm

2020 ◽  
Vol 10 (1) ◽  
pp. 422-430
Author(s):  
Faris Mohammad Abd ◽  
Mehdi Ebady Manaa
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Internet Of Things ◽  
Heart Diseases ◽  
Large Data ◽  
Machine Learning Algorithms ◽  
Healthcare Sector ◽  
Map Reduce ◽  
The Internet ◽  
The Internet Of Things

AbstractOver the last few years, the huge amount of data represented a major obstacle to data analysis. Big data implies that the volume of data undergoes a faster progress than computational speeds, thereby demanding a larger data storage capacity. The Internet of Things (IoT) is a main source of data that is closely related to big data, as the former extends to a variety of fields such as healthcare, entertainment, and disaster control. Despite the different advantages associated with the composition of Big Data analytics and IoT, there are a number of complex difficulties and issues involved that need to be resolved and managed to ensure an accurate data analysis. Some of these solutions include the utilization of map-reduce techniques, processing, and large data scale, particularly for the relatively less time that this method requires to process large data from the Internet of Things. Machine learning algorithms of this kind are often implemented in the healthcare sector. Medical facilities need to be advanced so that more appropriate decisions can be made in terms of patient diagnosis and treatment options. In this work, two datasets have been used: the first set, used in the prediction of heart diseases, obtained an accuracy rate of 84.5 for RF and 83 for J48, whereas the second dataset is related to weather stations (automated sensors) and obtained accuracy rates of 88.5 and 86.5 for RF and J48, respectively.

Fundamentals of Wireless Sensor Networks Using Machine Learning Approaches

2020 ◽  
pp. 233-254
Author(s):  
E. B. Priyanka ◽  
S. Thangavel ◽  
D. Venkatesa Prabu
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Real Time ◽  
Oil And Gas ◽  
Predictive Analytics ◽  
Oil And Gas Industry ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Learning Approaches ◽  
Gas Industry

Big data and analytics may be new to some industries, but the oil and gas industry has long dealt with large quantities of data to make technical decisions. Oil producers can capture more detailed data in real-time at lower costs and from previously inaccessible areas, to improve oilfield and plant performance. Stream computing is a new way of analyzing high-frequency data for real-time complex-event-processing and scoring data against a physics-based or empirical model for predictive analytics, without having to store the data. Hadoop Map/Reduce and other NoSQL approaches are a new way of analyzing massive volumes of data used to support the reservoir, production, and facilities engineering. Hence, this chapter enumerates the routing organization of IoT with smart applications aggregating real-time oil pipeline sensor data as big data subjected to machine learning algorithms using the Hadoop platform.

Application of Machine Learning Algorithms for Managing Well Integrity in Gas Lift Wells

2021 ◽  
Author(s):  
Adel Mohamed Salem Ragab ◽  
Mostafa Sa’eed Yakoot ◽  
Omar Mahmoud
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Oil And Gas ◽  
Predictive Analytics ◽  
Early Recognition ◽  
Petroleum Industry ◽  
Machine Learning Algorithms ◽  
Risk Level ◽  
Accurate Analysis ◽  
Well Integrity

Abstract Well integrity (WI) impairments in oil and gas (O&G) wells are one of the most formidable challenges in the petroleum industry. Managing WI for different groups of well services necessitates precise assessment of risk level. When WI classification and risk assessment are performed using traditional methods such as spreadsheets, failures of well barriers will result in complicated and challenging WI management, especially in mature O&G fields. Industrial practices, then, started moving toward likelihood/ severity matrices which turned out later to be misleading in many cases due to possibility of having skewness in failure data. Developing a reliable model for classifying level of WI impairment is becoming more crucial for the industry. Artificial intelligence (AI) includes advanced algorithms that use machine learning (ML) and computing powers efficiently for predictive analytics. The main objective of this work is to develop ML models for the detection of integrity anomalies and early recognition of well failures. Most common ML algorithms in data science include; random forest, logistic regression, quadratic discriminant analysis, and boosting techniques. This model establishment comes after initial data gathering, pre-processing, and feature engineering. These models can iterate different failure scenarios considering all barrier elements that could contribute to the WI envelope. Thousands of WI data arrays can be literally collected and fed into ML models after being processed and structured properly. The new model presented in this paper can detect different WI anomalies and accurate analysis of failures can be achieved. This emphasizes that managing overall risks of WI failures is a robust and practical approach for direct implementation in mature fields. It also, creates additional enhancement for WI management. This perspective will improve efficiency of operations in addition to having the privilege of universality, where it can be applicable for different well groups. The rising wave of digitalization is anticipated to improve field operations, business performance, and production safety.

scholarly journals A Comprehensive Big Data Analytics Based Framework for Premature Recognition of Breast Cancer

2020 ◽  
Vol 9 (3) ◽  
pp. 2188-2192
Keyword(s):  
Breast Cancer ◽  
Big Data ◽  
Data Analytics ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Massive Data ◽  
Healthcare Industry ◽  
Rapid Pace ◽  
Comprehensive Framework

Healthcare industry is fast growing and expanding in rapid pace. The volume and veracity of data generated in the industry is massive and requires huge storages and handling capability. Big data is empowered with such robust abilities and hence most suitable for handing large amount of data. Further, hese data could be utilized towards building predictive and forecasting models. Breast cancer is a deadly form of cancer majorly affecting women around the globe. The concept of big data and predictive analytics is being explored in the paper towards early diagnosis of breast cancer. This paper surveys various literatures available on application of big data analysis for breast cancer. Subsequently a comprehensive framework is being proposed based on the gaps identified. Different machine learning algorithms which can be applied in the framework is also detailed in the paper. Such frameworks when implemented will greatly help in handling the massive data available and aid in early detection of breast cancer.

scholarly journals Cardiac disease and pregnancy: hyper vigilance and extreme caution for optimal outcome

2020 ◽  
Vol 9 (2) ◽  
pp. 825
Author(s):  
Naresh Dhawan ◽  
Rohin Kumar ◽  
Reema Kumar Bhatt
Keyword(s):  
Aortic Valve ◽  
Bicuspid Aortic Valve ◽  
Cardiac Disease ◽  
Congenital Heart ◽  
Single Ventricle ◽  
Heart Diseases ◽  
Fetal Outcome ◽  
Coordinated Care ◽  
Extreme Caution ◽  
Congenital Cardiac Malformation

Cardiac disease in pregnancy is a leading cause of maternal death in more so high-income countries. The armamentarium for winning this difficult battle involves shared decision-making with communication across the clinical team and the patient. There is limited clinical evidence concerning effective approaches to managing such complex care and moreover involvement of different specialists makes coordinated care challenging. Bicuspid aortic valve (BAV) is the most common congenital cardiac malformation, occurring in 1-2% of the population whereas a single ventricle is a rare congenital heart disease that accounts for less than 1% of all congenital heart diseases. We had two cases of pregnancy with bicuspid aortic valve in one case and the other with single ventricle. The involvement of multidisciplinary team involving cardiologist, cardiothoracic anaesthetist and fetal maternal medicine specialist resulted in good maternal and fetal outcome in both the cases.

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