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Chapter 1 fyp.docx - Chapter 4: Third Deliverable For...

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Chapter 4: Third Deliverable For Object Oriented Approach4.1. Introduction:We are going to make an web application titled as “Disease Prediction OnSymptoms”. The main aim of the project is to facilitate the user through latesttechnology and increase the user experience that he not avails through other onlineresources. We provide different predictions relevant to diseases based on varioussymptoms the user is facing with him/her and evaluating the results by keeping ineye the previous dataset.Following artifacts must be included in the 3rd deliverables.1. Domain Model2. System Sequence Diagram3. Design Class Diagram4. Activity Chart Diagram5. State Chart DiagramNow we discuss these artifacts one by one as follows:
1.1 IntroductionThe Earth is going through a purplish patch of technology where the demand of intelligence andaccuracy is increasing behind it. Today’s people are likely addicted to internet but they are notconcerned about their physical health. People ignore the small problem and don’t visit to hospitalwhich turn into serious disease with time. Taking the advantage of this growing technology, ourbasis aim is to develop such a system that will predict the multiple diseases in accordance withsymptoms put down by the patients without visiting the hospitals / physicians.Machine Learning is a subset of AI that is mainly deal with the study of algorithms whichimprove with the use of data and experience. Machine Learning has two phases i.e. Training andTesting. Machine Learning provides an efficient platform in medical field to solve varioushealthcare issues at a much faster rate. There are two kinds of Machine Learning – SupervisedLearning and Unsupervised Learning. In supervised learning we frame a model with the help ofdata that is well labeled. On the other hand, unsupervised learning model learn from unlabeleddata.The fact that we can make estimations, predictions and give the ability for machines tolearn by themselves is both powerful and limitless in term of application possibilities. We canuse Machine Learning in Finance, Medicine, almost everywhere. That’s why we decided toconduct our project around the Machine Learning.
The intent is to deduce a satisfactory Machine Learning algorithm which is efficient and accuratefor the prediction of disease. In this paper, the supervised Machine Learning concept is used forpredicting the diseases. The main feature will be Machine Learning in which we will be usingalgorithms such as Decision Tree, Random Forest, Naive Bayes and KNN(nearest neighbor)which will help in early prediction of diseases accurately.Predicting diseases with real factors is the main crux of our research project. Here we aim tomake our evaluations based on every basic parameter that is considered while determining thedisease.

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