N Satyanandam

Work place: Department of CSE, Bhoj Reddy Engineering College for Women, Hyderabad, Telangana, India

E-mail: satya2606@gmail.com


Research Interests: Data Mining, Image Processing, Neural Networks, Computational Learning Theory


N Satyanandam is working as Associate Professor in the department of Computer Science and Engineering, Bhoj Reddy Engineering College for Women, Hyderabad, Telangana, India. He received B.Tech(CSE) in 1996 and MBA (MM) in 1999; both from Andhra University, Visakhapatnam and M.Tech (Computer Science& Engineering) in 2004 from JNTU, Hyderabad. He has 19 years of teaching experience. He is pursuing Ph.D in JNTUH-Hyderabad. He published 10 research papers in National and International Journals & Conferences. His research areas of interests are Data Mining& Warehousing, Machine Learning, Neural Networks, Digital Image Processing, . He is a Life Member of ISTE.

Author Articles
Heart Disease Detection Using Predictive Optimization Techniques

By N Satyanandam Ch. Satyanarayana

DOI: https://doi.org/10.5815/ijigsp.2019.09.02, Pub. Date: 8 Sep. 2019

Health care is a major research domain needed instantaneous solutions. Due to the digitalization of data in each and every domain it is becoming tedious to store and analysis. So, the demand of proficient algorithms for health care data analysis is also increasing. Predictive analytics is the major demand from the health care community to the computing researches in order to predict and reduce the potential health catastrophes. Parallel research attempts are made to predict the possibilities of the disease on the different health care domains at various regions. However, those attempts are limited and not remarkable to achieve the desired outcomes. Recently, in the field of data analytics; Machine Learning techniques became popular in generating optimized solutions with effective data processing capabilities. Henceforth, this research work considers the heart disease analysis using machine learning techniques to determine the disease severity levels. Experiments are made on UCI heart disease dataset and our results shows 92% accuracy the heart severity detection.

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