Heart Disease Detection using Big Data using ML
In this project, Automatic text summarization is basically summarizing the given paragraph using natural language processing and machine learning. There has been an explosion in the amount of text data from a variety of sources. This volume of text is an invaluable source of information and knowledge which needs to be effectively summarized to be useful. In this review, the main approaches to automatic text summarization are described. We review the different processes for summarization and describe the effectiveness and shortcomings of the different methods. The system works by assigning scores to sentences in the document to be summarized and using the highest-scoring sentences in the summary. Score values are based on features extracted from the sentence. A linear combination of feature scores is used. Almost all of the mappings from feature to score and the coefficient values in the linear combination are derived from a training corpus. Some anaphor resolution is performed. The system was submitted to the Document Understanding Conference for evaluation. In addition to basic summarization, some attempt is made to address the issue of targeting the text at the user. The intended user is considered to have little background knowledge or reading ability. The system helps by simplifying the individual words used in the summary and by drawing the pre-requisite background information from the web.
Remote mobile health monitoring has already been recognized as not only a potential. Each stage such as data aggregation, data maintenance, data integration, data analysis, and pattern interpretation, application faces many challenges while dealing with healthcare big data (HBD). There are many problems in the complexity of analysis and scalable of data in parallelization computing model is processed. They have no accuracy in the prediction of heart disease.
- Certain approaches are applicable only for small data.
- Certain combinations of classifiers overfit with the data set while others are under fitted.
- Some approaches are not adoptable for real-time collection of the database implementation.
In our project, the proposed system is an accurate prediction of heart disease problems in health care applications. Easier to analyze the scalable of health care big data. Less time consumption with the efficiency of data in heart disease. High performance in data maintained of heart disease prediction.
- the performance classification of heart-based diseases is further improved.
- Time complexity and accuracy can have measured by various machine learning models so that we can measure different.
- Different machine learning has a high accuracy of results.
- Risky factors can be predicted early by machine learning models.
- Windows 7,8,10 64 bit
- RAM 4GB
- Data Set
- Python 2.7
- Anaconda Navigator
Python’s standard library
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