PREDICTING NEURODEGENERATIVE DISEASES
Neurodegenerative diseases are a heterogeneous group of disorders that are characterized by the progressive degeneration of the structure and function of the central nervous system or peripheral nervous system. These diseases cause your brain and nerves to deteriorate over time. It can change your personality and cause confusion. It also may destroy your brain’s tissue and nerves by causing permanent damage, so symptoms tend to get worse as the disease progresses. New symptoms are also likely to develop over time. There’s no cure for neurodegenerative diseases, but treatment can still help. Treatment for these diseases tries to reduce symptoms and maintain quality of life. Treatment often involves the use of medications to control symptoms.
Some of the most common symptoms of neurodegenerative diseases include: memory loss, forgetfulness, apathy, anxiety, agitation, a loss of inhibition, mood changes etc.
Some brain diseases, such as Alzheimer’s disease, may develop as you age. They can slowly impair your memory and thought processes. Other diseases, such as Tay-Sachs disease, are genetic and begin at an early age:Read more for detailed analysis
My Research Project
In my research project i have done on the area of Internet of Things (IoT) domain, with an objectives of realizing that how IoT can be applied for environment specially for maintaining air quality in a specified location.
My Research project is titled as “Smart Real-Time Indoor Air Quality Sensing System and Analytics”
Abstract:- Indoor air quality monitoring and analytics is one of the important interdisciplinary research areas, which is attracting significant attention of various researchers from environment, mathematics, material science and electrical and computer engineering. According to a research study conducted by World Health Organization (WHO), pollution of indoor air is the most known hazardous case for respiratory diseases such as lung cancer, asthma and chronic diseases. Lack of information about the pollution sources and its serious impact on health leads to a huge number of people likely to be affected by various types of respiratory diseases. With the recent developments in sensing technology, machine learning and communication technology, IoT based Smart Real Time Indoor air quality sensing and analytics have been implemented to promote better awareness for users to alert them about indoor air quality to maintain the well being in their indoor environments. The paper provides a proof of concept on IoT based Indoor air quality sensing system and analytics. The data is collected for analyzing indoor air quality in various indoor settings such as kitchen for oily based cooking, living room for insecticide spray, and smoking and flour mill for detecting flour dust during crop grinding. We used J48 and Naïve Bayes machine learning algorithm to model the air quality status. Result shows that the Naïve Bayes Algorithm detects 99.11% and J48 algorithm detects 99.30 % accurately
The abstract description of the research project is shown here.