Heart Disease Prediction using machine learning.pptx

Heart Disease Prediction PPT

1. Heart Disease Prediction

2. Abstract/Area of Domain With big data growth in biomedical and healthcare communities , accurate analysis of medical data benefits early disease detection, patient care and community services . D ifferent regions exhibit unique characteristics of certain regional diseases, which may weaken the prediction of disease outbreaks. In this paper, we streamline machine learning algorithm for effective prediction of chronic disease. To overcome the difficulty of incomplete data, we use a latent factor model to reconstruct the missing data. We propose a new convolutional neural network based multimodal disease risk prediction ( CNN-MDRP) algorithm using structured and unstructured data from hospital.

3. Objective Today, heart failure diseases affect more people worldwide than other autoimmune conditions. Cardiovascular Diseases (CVDs) affect the heart and obstruct blood flow through the blood vessels. Chronic ailments in CVD include heart disease (heart attack), cerebrovascular diseases (strokes), congestive heart failure, and many more pathologies. Worldwide, CVDs kill around 17 million a year, and death rates due to heart diseases have increased after the COVID-19 pandemic.

4. Problem Definition   Existing scheme has some defects . data set is typically small, for patients and diseases with specific conditions the characteristics are selected through experience. these pre-selected characteristics maybe not satisfy the changes in the disease and its influencing factors . For patient’s examination data, there is a large number of missing data due to human error . Existing System uses only unstructured text data to predict whether the patient is at high-risk of cerebral infarction.

5. Motivation Several risk factors for manual heart disease prediction may include inactivity in a physical form, unhealthy eating habits, or even the consumption of alcohol. Preexisting conditions, age, chest pain level, blood test results, and several such factors can be ensemble together computationally for heart disease prediction.

6. Introduction The healthcare problem of chronic diseases is also very important in many countries. With the development of big data analytics technology, more attention has been paid to disease prediction from the perspective of big data analysis, various researches have been conducted by selecting the characteristics automatically from a large number of data to improve the accuracy of risk classification. we combine the structured and unstructured data in healthcare field to assess the risk of disease. The goal is to predict whether a patient is amongst the cerebral infarction high-risk population according to their medical history . A novel CNN-based multimodal disease risk prediction (CNN-MDRP) algorithm for structured and unstructured data. The disease risk model is obtained by the combination of structured and unstructured features.

7. Architecture Diagram

8. Existing System Qiu et al. proposed an optimal big data sharing algorithm to handle the complicate data set in telehealth with cloud techniques . One of the application is to identify high-risk patients which can be utilized to reduce medical cost since high-risk patients often require expensive healthcare. I t innovatively brought forward the concept of prediction-based healthcare applications, including health risk assessment.

9. Proposed System Dataset collection is collecting data which contains patient details. Attributes selection process selects the useful attributes for the prediction of heart disease. After identifying the available data resources, they are further selected, cleaned, made into the desired form. Different classification techniques as stated will be applied on preprocessed data to predict the accuracy of heart disease. Accuracy measure compares the accuracy of different classifiers.

10. Proposed System

11. Feasibility Study In this paper, we have checked the following study to be true: Technical Feasibility Operational Feasibility Economical Feasibility Social Feasibility Management Feasibility Legal Feasibility Time Feasibility

12. SRS H/W System Configuration: - Processor - Pentium –III/intel i3,i5,i7 RAM - 1 GB (min) Hard Disk - 20 GB Key Board - Standard Windows Keyboard Mouse - Two or Three Button Mouse Monitor - SVGA

13. SRS S/W System Configuration:- Operating System : Windows 8/8.1/10 Application Server : Tomcat7.0/8.X Front End : HTML, Jsp Scripts : JavaScript. Server side Script : Java Server Pages. Database : MySQL Database Connectivity : JDBC

14. Literature Survey Sr. no. Publication Author Name Paper Name Year Objective Limitation 1 MIS Quarterly Hsinchun Chen Roger H. L. Chiang Veda C. Storey BUSINESS INTELLIGENCE AND ANALYTICS: FROM BIG DATA TO BIG IMPACT 2012 To serve, in part, as a platform and conversation guide for examining how the IS discipline can better serve the needs of business decision makers in light of maturing and emerging BI&A technologies, ubiquitous Big Data, and the predicted shortages of data-savvy managers and of business professionals with deep analytical skills.   Need of new analytical skills for Business Intelligence. 2 Human Language Technologies Wenpeng Yin and Hinrich Sch ¨ utze Convolutional Neural Network for Paraphrase Identification 2015 To present a new deep learning architecture Bi-CNN-MI for paraphrase identification (PI). Bi-CNN-MI not able to match the sentence, question answering and other tasks.

15. Literature Survey Sr. no. Publication Author Name Paper Name Year Objective Limitation 3. Joel Brooks, Matthew Kerr, Matthew Kerr Joel Brooks Matthew Kerr, John Guttag Developing a Data-Driven Player Ranking in Soccer Using Predictive Model Weights 2016 Present a novel method of utilizing soccer event data to understand the relationship between pass location and shot opportunities. And predict whether a possession will end in a shot. Sequential in- formation does not give a more detailed understanding of how passing strategy relates to outcomes . 4 IEEE Senjuti Basu Royy , Ankur Teredesaiy , Kiyana Zolfaghary , Rui Liuy , David Hazely ?, Stacey Newmany , Albert Marinez Dynamic Hierarchical Classification for Patient Risk-of-Readmission 2015 Provides framework to clearly outperforms baseline solutions for congestive heart failure (CHF). DHC automatically discovers and defines the layers by leveraging the underlying historical patient data. Different data distributions and classifiers at each layer can lead to different probability distribution which may cause some inconsistency in final predictions .

16. Literature Survey Sr. no. Publicaton Author Name Paper Name Year Objective Limitation 5 IEEE Min Chen, Ping Zhou, and Giancarlo Fortino Emotion Communication System 2016 emotion communication protocol, which provides a high-level reliable support for the realization of emotion communications. There is delay in two modes in emotion communication system,

17. Project Plan

18. Thank You!!