Impact of Lifestyle Factors on Life Expectancies in Pakistan

Abstract

The main idea of my research work is to perform data analytics on the data collected by monitoring the daily life styles of people. These analytics will help identify and timely detect any disease. With the recent advancements in sensor technologies, wearable devices, Internet of Things and wireless communication the research on mobile health care and monitoring systems has reached new levels. This system enables continuous monitoring of patients’ physiological and health conditions by sensing and transmitting measurements like heart rate, physical activity, blood pressure, blood sugar levels and nutritional values. My thesis idea revolves around collecting the real time data of people, cleaning it and then performing medical big data analytics on it. This research will help in diagnosis and treatment of patients with chronic diseases like depression, hypertension and diabetes. This research can also be used for monitoring the health of elderly people. After the collection of real time data for analysis, the next step will be Data Fusion. In these systems, the data collected is then integrated with informational databases, knowledge based systems and other similar sources. The basic purpose of Data Fusion is to integrate all types if information in a uniform format which can be used for computational purposes. In the next step, I will be performing data analytics on the processed data using different big data tools and algorithms. In this study, I will be merging health care with technology to create smart and cost effective solution for early stage detection and prevention of diseases. My thesis work includes acquiring the data, processing it, storing it and then through advance algorithms of information retrieval and data mining perform data analytics on the preprocessed dataset. The daily routine factors that will be the main focus of my work are: a) Sleep b) Nutrition  Fruit Intake  Water Intake c) Physical Activity d) Heart rate The health of patients can be now monitored through sensing wearable. The data collected through these wearable can be further utilized to extract patterns and form associations which can be used for early diagnosis of many diseases. Most of these illnesses are a result of our daily life routines. Making certain amendments in our routines can lead us towards a healthy life. The work of my thesis revolve around eliciting certain patterns from a real time data set of people’s daily routine and identifying the factors that can lead to certain illnesses or could disrupt the mental and physical health.