Prophecy Oxygen Levels at High Altitudes Using Fuzzy C-means Clustering

Abstract

Data Analysis is an efficient technique to implement the analysis on an immense
data, which can be structured, semi-structured or even unstructured. Various advanced
data analysis techniques from statistics, artificial intelligence and others can be used for
analyzing data in the areas of medical diagnosis, user pattern extraction, image extraction,
market research, cell segmentation and spatial data extraction. Predictive analysis is one of
the methods in data analysis used to identify the predictions on future happenings which
are not known in advance exactly. The key benefits of predictive analysis are preventing
risks, managing resources, and strategic decision making. This paper focuses on the benefit
of preventing risks factors by analyzing environment at high altitude areas through
predicting the oxygen levels using Fuzzy C Means algorithm. The percentage of oxygen
level is not same at sea level as it is in hilly areas which have high altitudes. Less oxygen in
atmosphere may lead to short of breath causes chronic illness to people in all age groups.
This study focuses on the collection and processing of data, identifying the prediction
model, the results and the improvisation in the future work.