AN OVERVIEW OF A BIG DATA MANAGEMENT AND THE PERSONAL PRIVACY CONSTRAINT

Big Data refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage and analyze. The accumulated huge amount of data that previously of no significant importance or value have been put into maximum use due to the availability of newly designed Big Data tools that surpass earlier available data mining tools. Big Data is now of tremendous importance to organizations and data mining researchers because better results are gotten from larger volume of data. Predictions and Analysis of business are becoming more accurate and interesting with the advent of Big Data Tools. The scale and scope of changes that Big Data are bringing about are at an inflection point, set to expand greatly, as a series of technology trends accelerate and courage. Data have always been part of information Technology (IT), and then the birth of Big Data is a plus to the IT profession. In this paper, we introduced readers to the concept of Big Data, the various sources of data for Big Data. Some of the advantages and applications that have been successfully implemented using Big Data tools. Some of the challenges of Big Data were also discussed with special reference to the most crucial of these challenges- the privacy or personal privacy issue which if not well managed could bring an individual or an entire organization using Big Data down. This paper is written to create awareness to researchers and to sensitize the existing and intending users of Big Data tools of the privacy issue and possible measures that can be of assistance

A Review of Big Data Management, Benefits and Challenges

Big Data is relatively a new concept which refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage and analyze. The accumulated huge amount of data that previously of no significant importance or value have been put into maximum use due to the availability of newly designed Big Data tools that surpass earlier available data mining tools. Big Data is now of tremendous importance to organizations and data mining researchers because better results are gotten from larger volume of data. Predictions and Analysis of business are becoming more accurate and interesting with the advent of Big Data Tools. The scale and scope of changes that Big Data are bringing about are at an inflection point, set to expand greatly, as a series of technology trends accelerate and courage. In this paper, we introduced readers to the concept of Big Data, the various sources of data for Big Data. Some of the advantages and applications that have been successfully implemented using Big Data tools. Some of the challenges of Big Data were also discussed with special reference to the most crucial of these challenges- the personal privacy issue which if not well managed could bring an individual or an entire organization using Big Data down. This paper aims to create awareness to researchers and to sensitize the existing and intending users of Big Data tools of the privacy issue and possible measures that can be of assistance

A Framework for Mobile Health Management for Diseases in Nigeria with Benefits and Challenges

World Health Organization (WHO) estimates that African Region has a shortfall of 817,992 health workers. Sub-Saharan Africa faces the greatest challenges with 11% of the world’s population and 24% of the global burden of diseases; the region has only 3% of the world’s health workers commanding less than 1% of world health expenditure. The ratio of available facilities to the teeming population is also grossly inadequate. These are among the major reasons why computer (Information Technology (IT)) has been employed over the years to help in different areas of health carediagnosis, treatment, monitoring and medical records. It is very obvious that most of these computer based health management systems lack among other things the real time live interaction between health practitioners and patients. Mobile applications are now increasingly popular, though used mostly for instant messaging and social networking. In this paper we have proposed the use of mobile application to improve health care delivery system in Nigeria with real-time live chatting mode similar to the instant social chatting where diagnosis, treatment and monitoring of patients can be done through mobile technology. While readers’ attention was drawn to this, a framework of the proposed system was developed for health practitioners and researchers to look into so as to move Nigeria and other developing countries in African forward in the health sector

A FRAME WORK FOR LIVE MOBILE HEALTH MANAGEMENT/ CARE SYSTEM FOR DISEASES IN NIGERIA

Health challenges are increasing daily while the ratio of health practitioners and available facilities to the teeming population is decreasing. According to the World Health Organization (WHO), the world is in shortage of more thanfour million health workers which has posed a problem to the existence of man. These are among the major reasons why computer (Information Technology (IT)) has been employed over the years to help in different areas of health care- diagnosis, treatment, monitoring and medical records. It is very obvious that most of these computer based health related systems lack among other things the real time live interaction between health practitioners and patients. Mobile applications are now increasingly popular, though used mostly for instant messaging and social networking. In this paper we have proposed the use of mobile application to improve health care delivery system in Nigeria withreal-time live chatting mode similar to the instant social chatting where diagnosis, treatment and monitoring of patients canbe done through mobile technology. While readers’ attention was drawn to this, a framework of the proposed system was developed for health practitioners and researchers to look into so as to move Nigeria forward in the health sector

A Machine Learning Based Clinical Decision Support System for Diagnosis and Treatment of Typhoid Fever

Health challenges of the world increase daily while medical practitioners and medical facilities ratio to the increasing population is nothing to write home about. As revealed by World Health Organization (WHO), the world is in need of additional over four million medical practitioners. The few available medical facilities and medical personnel are concentrated in the urban centers which tend to make the situation worst in the rural areas. These among other challenges in the health sector make computer based diagnosis systems desirable. Typhoid fever otherwise known as Enteric fever is a trauma to most developing countries of the world with prevalent cases in Africa. It is on the record that more than six hundred thousand deaths occur annually as a result of typhoid fever. This number is very high due to many factors which include insufficient medical facilities, insufficient medical personnel, poor diagnosis and treatment. In this work, a new diagnosis and treatment system was developed to handle typhoid fever cases. A promising machine learning technique-decision tree algorithm was used on labeled set of typhoid fever conditional variables to generate a decision tree and classifiers for the diagnosis of typhoid fever and treatments were provided according to the level of severity of the disease. The accuracy of the system was measured on both the training set and testing set with the detection rates of 100% and 95% respectively. The system was implemented using Visual Basic as front end and MySQL as backend

A Machine Learning Approach to Clinical Diagnosis of Typhoid Fever

Typhoid fever is one of the major life threatning diseases, accounting for the death of millions of people every year apart from contributing to economic backwardness, mostly in Africa. Prompt and accurate diagnosis is a major key in the medical field, the large number of deaths associated with typhoid fever is as a result of many factors which include: poor diagnosis, self medication, shortage of medical experts and insufficient health institutions. These prompted for the development of a typhoid diagnosis system that can be used by anyone of average intelligence as this will assist in quick diagnosis of the disease despite shortage of health institutions and medical experts. A machine learning technique was used on the labelled set of typhoid fever conditional variables to generate explainable rules for the diagnosis of typhoid fever. The labelled database was divided into five different levels of severity of typhoid fever and the classification accuracies on both the training set and testing set are 95% and 96% respectively. Implementation was carried out using Visual Basic as front end and MySQL as backend