Machine Learning Techniques Usage in Prediction of Multiple Chronical Diseases
AKKALA BHARGAVI
, MARADANA JAGADEESH KUMAR , BURI KISHOR KUMAR , M. MARY SUJATHA
Machine Learning Algorithms, Data Mining Techniques, Random oversampling, Chronical Disease Prediction
Multiple Chronical Disease Prediction System is an effective healthcare predictive application that aims to predict multiple chronical diseases including Chronical Kidney Disease, Heart Disease, Diabetes, Brain Stroke and Lung Cancer with the help of various machine learning algorithms. Scope of the project is all-inclusive, focussing to predict the possibility of various diseases in the human beings taking into account their distinctive health profiles. The electronic health records are acquired from the online sources like UCI machine learning repository, GitHub and Kaggle. This prediction system uses data mining techniques for completing the purpose i.e., data pre-processing and is trained on various ensemble methods like random forest, supervised techniques, unsupervised techniques like decision tree, logistic regression, multi-layer perceptron and naive bayes. As most of these diseases share some common risk factors through our work, we are trying to explore the possible interconnection between these chronical diseases and also the chance of developing these chronical illnesses. Finally, this prediction system with the power of machine learning techniques helps in the identification and prognosis of such diseases at early stages to prevent the extremity of them and at the same time reducing the health care expenditure.
"Machine Learning Techniques Usage in Prediction of Multiple Chronical Diseases", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.9, Issue 4, page no.667 - 674, April-2024, Available :https://ijsdr.org/papers/IJSDR2404093.pdf
Volume 9
Issue 4,
April-2024
Pages : 667 - 674
Paper Reg. ID: IJSDR_210802
Published Paper Id: IJSDR2404093
Downloads: 000347300
Research Area: Computer Science & Technology
Country: TIRUPATI, ANDHRAPRADESH, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
