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Paper Title : A MODEL FOR LIFESTYLE DISEASE ALZHEIMER'S PREDICTION USING K-NEAREST NEIGHBORS

ISSN : 2395-1303
Year of Publication : 2022
10.5281/zenodo.6894658

Authors: T.RADHIKA, P.SHREYA, G. AKHILSAI

         


Abstract
If you decide to check out that offer for a vacation. You browse through the travel agency website and search for a hotel. When you look at a specific hotel, just below the hotel description there is a section titled “You might also like thesehotels”. This is a common use case of Machine Learning called “Recommendation Engine”. Again, many data points were used to train a model in order to predict what will be the best hotels to show you under that section, based on a lot of information theyalready know about you.So if you want your program to predict, for example, traffic patterns at a busy intersection (task T), you can run it through a machine learning algorithm with data about past traffic patterns (experience E) and, if it has successfully “learned”, it will then do better at predicting future traffic patterns (performance measure P). The highlycomplex nature of many real-world problems, though, often means that inventing specialized algorithms that will solve them perfectly every time is impractical, if not impossible. Examples of machine learning problems include, “Is this cancer?”, “Whichof these people are good friends with each other?”, “Will this person like this movie?” such problems are excellent targets for Machine Learning, and in fact machine learninghas been applied such problems with great success.

Reference
1. https://www.ritchieng.com/machine- learning-k-nearest-neighbors-knn/ 2. https://www.youtube.com/watch?v= 6kZ-OPLNcgE 3. https://www.mayoclinic.org/diseases -conditions/alzheimers- disease/symptoms-causes/syc- 20350447 4. http://profsite.um.ac.ir/~monsefi/ma chine-learning/pdf/Machine- Learning-Tom-Mitchell.pdf 5. https://www.kaggle.com/jboysen/mri -and-alzheimers/metadata
Keywords
- A MODEL FOR LIFESTYLE DISEASE ALZHEIMER'S PREDICTION USING K-NEAREST NEIGHBORS