following topics are discussed inside the PPT:
Introduction
Objective
Motivation
Literature Survey
Some Key Features of Disease
Plan of Action
Methodology Adopted
Data Collection
Steps to be Performed
Functional Architecture
Existing model uses structured data to predict the patients of either high risk or low risk.
But for a complex disease, structured data is not a good way to describe the disease.
We propose a new convolutional neural network (CNN)-based multimodal disease risk prediction algorithm using structured and unstructured data from hospital.
In this paper, we mainly focus on the risk prediction of cerebral infarction.
Machine Learning for Disease PredictionMustafa Oğuz
A great application field of machine learning is predicting diseases. This presentation introduces what is preventable diseases and deaths. Then examines three diverse papers to explain what has been done in the field and how the technology works. Finishes with future possibilities and enablers of the disease prediction technology.
We are predicting Heart Disease by Taking 14 Medical Parameters as an inputs through 2 data Minning Techniques(Decision Tree(Faster) And KNN neighbour Algorithms(Slower)).
And Visualizing The dataset.If the output 1 then it means Higher Chances of getting Heart Attack ,if 0 then it means Less chances of Heart Attack.
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
Data mining techniques are used for a variety of applications. In healthcare industry, datamining plays an important
role in predicting diseases. For detecting a disease number of tests should be required from the patient. But using data
mining technique the number of tests can be reduced. This reduced test plays an important role in time and performance.
This report analyses data mining techniques which can be used for predicting different types of diseases. This report reviewed
the research papers which mainly concentrate on predicting various disease
Existing model uses structured data to predict the patients of either high risk or low risk.
But for a complex disease, structured data is not a good way to describe the disease.
We propose a new convolutional neural network (CNN)-based multimodal disease risk prediction algorithm using structured and unstructured data from hospital.
In this paper, we mainly focus on the risk prediction of cerebral infarction.
Machine Learning for Disease PredictionMustafa Oğuz
A great application field of machine learning is predicting diseases. This presentation introduces what is preventable diseases and deaths. Then examines three diverse papers to explain what has been done in the field and how the technology works. Finishes with future possibilities and enablers of the disease prediction technology.
We are predicting Heart Disease by Taking 14 Medical Parameters as an inputs through 2 data Minning Techniques(Decision Tree(Faster) And KNN neighbour Algorithms(Slower)).
And Visualizing The dataset.If the output 1 then it means Higher Chances of getting Heart Attack ,if 0 then it means Less chances of Heart Attack.
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
Data mining techniques are used for a variety of applications. In healthcare industry, datamining plays an important
role in predicting diseases. For detecting a disease number of tests should be required from the patient. But using data
mining technique the number of tests can be reduced. This reduced test plays an important role in time and performance.
This report analyses data mining techniques which can be used for predicting different types of diseases. This report reviewed
the research papers which mainly concentrate on predicting various disease
Disease prediction and doctor recommendation systemsabafarheen
This paper will tell you how the system will work in terms of disease prediction also will suggest you nearest hospital with experienced doctors, cheap fees
DISEASE PREDICTION BY MACHINE LEARNING OVER BIG DATA FROM HEALTHCARE COMMUNI...Nexgen Technology
GET IEEE BIG DATA,JAVA ,DOTNET,ANDROID ,NS2,MATLAB,EMBEDED AT LOW COST WITH BEST QUALITY PLEASE CONTACT BELOW NUMBER
FOR MORE INFORMATION PLEASE FIND THE BELOW DETAILS:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com
Mobile: 9791938249
Telephone: 0413-2211159
www.nexgenproject.com
A major challenge facing healthcare organizations (hospitals, medical centers) is
the provision of quality services at affordable costs. Quality service implies diagnosing
patients correctly and administering treatments that are effective. Poor clinical decisions
can lead to disastrous consequences which are therefore unacceptable. Hospitals must
also minimize the cost of clinical tests. They can achieve these results by employing
appropriate computer-based information and/or decision support systems.
Most hospitals today employ some sort of hospital information systems to manage
their healthcare or patient data.
These systems are designed to support patient billing, inventory management and generation of simple statistics. Some hospitals use decision support systems, but they are largely limited. Clinical decisions are often made based on doctors’ intuition and experience rather than on the knowledge rich data hidden in the database.
This practice leads to unwanted biases, errors and excessive medical costs which affects the quality of service provided to patients.
Diabetes Prediction Using Machine Learningjagan477830
Our proposed system aims at Predicting the number of Diabetes patients and eliminating the risk of False Negatives Drastically.
In proposed System, we use Random forest, Decision tree, Logistic Regression and Gradient Boosting Classifier to classify the Patients who are affected with Diabetes or not.
Random Forest and Decision Tree are the algorithms which can be used for both classification and regression.
The dataset is classified into trained and test dataset where the data can be trained individually, these algorithms are very easy to implement as well as very efficient in producing better results and can able to process large amount of data.
Even for large dataset these algorithms are extremely fast and can able to give accuracy of about over 90%.
Predictive Analysis of Breast Cancer Detection using Classification AlgorithmSushanti Acharya
Dissertation project titled “Predictive analysis of Breast Cancer detection using Classification”. For the research conducted, Breast Cancer Wisconsin Diagnostics dataset was used for analysis. Using R language machine learning model was designed based on various algorithms and the derived results were then visualized to present the most accurate model of them all (SVM in this case).
Machine Learning - Breast Cancer DiagnosisPramod Sharma
Machine learning is helping in making smart decisions faster. In this presentation measurements carried out on FNAC was analysed. The results were validated using 20 percent of the data. The data used for POC is from UCI Repository/
Cardiovascular Disease Prediction Using Machine Learning Approaches.pptxTaminul Islam
Cardiovascular Disease Prediction Using Machine Learning Approaches.
Presentation for CISES 2023. Presentation Outline.
Introduction
Objectives
Literature review
Data Collection
Methodology
Result
Challenges & Future work
Conclusion
Predictive Analytics and Machine Learning for Healthcare - DiabetesDr Purnendu Sekhar Das
Machine Learning on clinical datasets to predict the risk of chronic disease conditions like Type 2 Diabetes mellitus beforehand; as well as predicting outcomes like hospital readmission using EMR RWE data.
A Heart Disease Prediction Model using Logistic Regression By Cleveland DataBaseijtsrd
The early prognosis of cardiovascular diseases can aid in making decisions to lifestyle changes in high risk patients and in turn reduce their complications. Research has attempted to pinpoint the most influential factors of heart disease as well as accurately predict the overall risk using homogenous data mining techniques. Recent research has delved into amalgamating these techniques using approaches such as hybrid data mining algorithms. This paper proposes a rule based model to compare the accuracies of applying rules to the individual results of logistic regression on the Cleveland Heart Disease Database in order to present an accurate model of predicting heart disease. K. Sandhya Rani | M. Sai Chaitanya | G. Sai Kiran"A Heart Disease Prediction Model using Logistic Regression By Cleveland DataBase" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11402.pdf http://www.ijtsrd.com/computer-science/data-miining/11402/a-heart-disease-prediction-model-using-logistic-regression-by-cleveland-database/k-sandhya-rani
Disease prediction and doctor recommendation systemsabafarheen
This paper will tell you how the system will work in terms of disease prediction also will suggest you nearest hospital with experienced doctors, cheap fees
DISEASE PREDICTION BY MACHINE LEARNING OVER BIG DATA FROM HEALTHCARE COMMUNI...Nexgen Technology
GET IEEE BIG DATA,JAVA ,DOTNET,ANDROID ,NS2,MATLAB,EMBEDED AT LOW COST WITH BEST QUALITY PLEASE CONTACT BELOW NUMBER
FOR MORE INFORMATION PLEASE FIND THE BELOW DETAILS:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com
Mobile: 9791938249
Telephone: 0413-2211159
www.nexgenproject.com
A major challenge facing healthcare organizations (hospitals, medical centers) is
the provision of quality services at affordable costs. Quality service implies diagnosing
patients correctly and administering treatments that are effective. Poor clinical decisions
can lead to disastrous consequences which are therefore unacceptable. Hospitals must
also minimize the cost of clinical tests. They can achieve these results by employing
appropriate computer-based information and/or decision support systems.
Most hospitals today employ some sort of hospital information systems to manage
their healthcare or patient data.
These systems are designed to support patient billing, inventory management and generation of simple statistics. Some hospitals use decision support systems, but they are largely limited. Clinical decisions are often made based on doctors’ intuition and experience rather than on the knowledge rich data hidden in the database.
This practice leads to unwanted biases, errors and excessive medical costs which affects the quality of service provided to patients.
Diabetes Prediction Using Machine Learningjagan477830
Our proposed system aims at Predicting the number of Diabetes patients and eliminating the risk of False Negatives Drastically.
In proposed System, we use Random forest, Decision tree, Logistic Regression and Gradient Boosting Classifier to classify the Patients who are affected with Diabetes or not.
Random Forest and Decision Tree are the algorithms which can be used for both classification and regression.
The dataset is classified into trained and test dataset where the data can be trained individually, these algorithms are very easy to implement as well as very efficient in producing better results and can able to process large amount of data.
Even for large dataset these algorithms are extremely fast and can able to give accuracy of about over 90%.
Predictive Analysis of Breast Cancer Detection using Classification AlgorithmSushanti Acharya
Dissertation project titled “Predictive analysis of Breast Cancer detection using Classification”. For the research conducted, Breast Cancer Wisconsin Diagnostics dataset was used for analysis. Using R language machine learning model was designed based on various algorithms and the derived results were then visualized to present the most accurate model of them all (SVM in this case).
Machine Learning - Breast Cancer DiagnosisPramod Sharma
Machine learning is helping in making smart decisions faster. In this presentation measurements carried out on FNAC was analysed. The results were validated using 20 percent of the data. The data used for POC is from UCI Repository/
Cardiovascular Disease Prediction Using Machine Learning Approaches.pptxTaminul Islam
Cardiovascular Disease Prediction Using Machine Learning Approaches.
Presentation for CISES 2023. Presentation Outline.
Introduction
Objectives
Literature review
Data Collection
Methodology
Result
Challenges & Future work
Conclusion
Predictive Analytics and Machine Learning for Healthcare - DiabetesDr Purnendu Sekhar Das
Machine Learning on clinical datasets to predict the risk of chronic disease conditions like Type 2 Diabetes mellitus beforehand; as well as predicting outcomes like hospital readmission using EMR RWE data.
A Heart Disease Prediction Model using Logistic Regression By Cleveland DataBaseijtsrd
The early prognosis of cardiovascular diseases can aid in making decisions to lifestyle changes in high risk patients and in turn reduce their complications. Research has attempted to pinpoint the most influential factors of heart disease as well as accurately predict the overall risk using homogenous data mining techniques. Recent research has delved into amalgamating these techniques using approaches such as hybrid data mining algorithms. This paper proposes a rule based model to compare the accuracies of applying rules to the individual results of logistic regression on the Cleveland Heart Disease Database in order to present an accurate model of predicting heart disease. K. Sandhya Rani | M. Sai Chaitanya | G. Sai Kiran"A Heart Disease Prediction Model using Logistic Regression By Cleveland DataBase" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11402.pdf http://www.ijtsrd.com/computer-science/data-miining/11402/a-heart-disease-prediction-model-using-logistic-regression-by-cleveland-database/k-sandhya-rani
Improving Care: More Method, Less Uncertainty, Impact summit 30 October 2013NHS Improving Quality
Improving Care: More Method, Less Uncertainty, Impact summit
30 October 2013
Improving Care: More Method, Less Uncertainty – Impact Summit, the second full day event in the Measurement Masterclass series, took place at the Central Hall Westminster in London on 30 October. The event was opened by Professor Sir Bruce Keogh and NHS IQ’s own Professor Moira Livingston, and included contributions from experts from across England and a virtual appearance by Dr Bob Lloyd.
This series for senior clinical leaders was developed to help increase the understanding of the principles of measurement for improvement. Designed to stimulate and challenge, it is supporting clinical leads in holding influential discussions with policy makers and data collectors.
To take the series forward and promote measurement for improvement more widely, NHS Improving Quality is setting up an advisory group to design and develop more learning resources for senior clinicians and their teams
More information: http://www.nhsiq.nhs.uk/capacity-capability/measurement-masterclass.aspx
ppt presentation for diabetes prediction using machine learning,
This is a classification problem of supervised machine learning. The objective is to predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset.
Machine learning can help people make a preliminary judgment about diabetes according to their daily physical examination data, and it can serve as a reference for doctors .
So in this study, LogisticRegression and DecisionTree are implemented to predict the diabetes
Primary care-based, teleretinal-screening protocol (Los Angeles Safety Net) UCLA CTSI
UCLA CTSI-Los Angeles County Department of Health Services (DHS) Projects
Principal Investigators: Lauren Daskivich (DHS), Carol Mangione (UCLA)
Diabetic retinopathy (DR) is the leading cause of blindness among working-age Americans, and among Los Angeles Latinos—the ethnic majority of patients in the Los Angeles County (LAC) safety net—the prevalence of DR is ~50%. Despite evidence that early detection and treatment can prevent blindness from DR, a significant number of persons with diabetes in our system fail to receive annual screening examinations and/or sight-saving treatments due to lack of access to specialty care. To date, the effect of a system level intervention on improving access to eye care and definitive treatment for diabetic retinopathy in an urban medically underserved, or safety net, population has not been evaluated. The objective of this project is to evaluate the impact of teleretinal screening on access to specialty ophthalmic care for diabetic patients in LAC who need monitoring or treatment for diabetic retinopathy. We propose a pre-post analysis of the LAC teleretinal screening implementation, and we aim to evaluate the number of patients screened for diabetic retinopathy, the number presenting for timely ophthalmic follow-up care and treatment, and the cost of the program.
How evidence affects clinical practice in egyptWafaa Benjamin
Evidence based medicine is the gold standard for clinical care.
It implies the integration of best research evidence with clinical expertise and patient values.
There is still a wide gap between availability of evidence and its incorporation into routine practice in our country.
Barriers to implementation could be personal, social, institutional, financial and legal barriers.
True practice of evidence based care can only occur where evidence based decisions coincide with patients’ beliefs and clinicians’ preferences.
Continuing medical education programs should be set with integrating evidence based medicine teaching and learning within clinical training.
The importance of presence of local national guidelines which need to take into account variation in expertise, resources and patient preferences across our geographical and cultural contexts .
Customisation of a guideline to meet the local needs of a target patient population is critical to successful implementation.
Methods for Observational Comparative Effectiveness Research on Healthcare De...Marion Sills
Research Objective: The SAFTINet project was funded by the AHRQ to build a distributed network of existing clinical and claims data that would support comparative effectiveness research (CER), with a focus on underserved populations and healthcare delivery system (HDS) characteristics. Observational research methods are appropriate, but require detailed protocols with a priori hypotheses and analytic plans. SAFTINet research specifically concerns the effects of a discrete set of HDS features (those often included in Patient-Centered Medical Home (PCMH) models) on health outcomes for primary care patients with asthma, hypertension, and hypercholesterolemia. Our objective is to present a description of this study’s measurement challenges, and to specify a priori hypotheses, analytic strategies, and plans for addressing bias and confounding for our asthma cohorts.
Study Design: An observational, longitudinal cohort study of primary care patients with asthma, with both secondary use of existing clinical and claims data and primary data collection for HDS features and patient- reported outcomes.
Population Studied: Our sample consists of 59 primary care practices in 5 healthcare organizations in Colorado, Utah and Tennessee; all practices serve underserved populations. These practices care for about 275,000 patients per year, of whom an estimated 22,000 have a diagnosis of asthma.
Principal Findings: We will present the processes used to define and measure the HDS features, covariates and asthma outcomes, along with planned analysis. Challenges include valid measurement of a multi-faceted HDS “exposure” variable, the inability to identify exposure onset, and the non-dichotomous nature of HDS characteristics. To measure HDS characteristics, we created a practice-level survey assessing 9 PCMH domains, including care coordination, specialty care and mental health integration, and patient-centeredness, as well as asthma-specific HDS characteristics (e.g., the use of asthma registries). Asthma outcomes included (1) those available as a result of routine electronic documentation of clinical care and claims administration (utilization indicative of an exacerbation), and (2) patient reported outcomes tools (Asthma Control Test). We used directed acyclic graphs to identify potential confounders of the relationship between HDS characteristics and asthma control, as well as other potential biases. The analytic plan is based on linear mixed effects models. Perspectives of the CER team, the technology team and the community engagement group were considered in the operationalization of all variables.
Conclusions: The design of rigorous observational CER observational CER should recognize the need for an intense planning phase. In accordance with good practice guidance for observational studies, an important component of the planning phase is to disseminate and obtain feedback on the research design in advance of its conduct.
Professor Martin Wiseman presentation on The Continuous Update Project: Novel approach to reviewing mechanistic evidence on diet, nutrition, physical activity and cancer at FENS European Nutrition Conference, 20-23 October 2015 Berlin (Germany).
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)
DISEASE PREDICTION SYSTEM USING DATA MINING
1. DISEASE PREDICTION SYSTEM USING
DATA MINING
Under Guidance
of
asst. prof. Ashutosh Pandey
Presented By:-
Anand kumar mishra (1616210020)
Siddhesh shukla
(1616210112)
Shivani yadav
(1616210103)
B.Tech. (CS) – 4th year.
2. OUTLINE
• Introduction
▪ Objective
▪ Motivation
• Literature Survey
• Some Key Features of Disease
• Plan of Action
• Methodology Adopted
▪ Data Collection
▪ Steps to be Performed
▪ Functional Architecture
• Expected Result
• Conclusion
• References
3. OBJECTIVE
Identifying hidden patterns and relationships
among various attributes that can lead to:
▪ better diagnosis,
▪ better medicines,
▪ better treatment
▪ Early diagnosis may predict the chances of
Disease and lead to take
preventive measures before the situation
becomes critical.
4. MOTIVATION
• The prevalence of Diabetes is increasing in all
countries and its prevention has become a public
health priority.
• The predictors of Diabetes risk are insufficiently
understood.
5. WORLDWIDE STATISTICS
❏ The number of people with diabetes has risen
from 108 million in 1980 to 422 million in 2014.
❏ The global prevalence of diabetes among adults
over 18 years of age has risen from 4.7% in 1980
to 8.5% in 2014
❏ Diabetes is a major cause of blindness, kidney
failure, heart attacks, stroke and lower limb
amputation.
❏ In 2016, an estimated 1.6 million deaths were
directly caused by diabetes.
❏ Almost half of all deaths attributable to high
blood glucose occur before the age of 70 years.
6.
7. INDIAN STATISTICS
� Diabetes currently affects more than 62 million
Indians, which is more than 7.1% of the adult
population.
� Nearly 1 million Indians die due to diabetes
every year.
TOTAL
DEATHS
(in million)
SEX % DEATH DUE
TO DIABETES
40.2 MALE 8
23.8 FEMALE 12
8. MOTIVATION
❖ Recent research has shown that the onset of can be
postponed or prevented with lifestyle intervention or by
medication.
❖ Identifying individuals at high risk of cancer has therefore
become a priority for targeting preventive measures
effectively.
❖ Symptoms are often less marked, thus the disease may be
diagnosed several years after onset, once complications
have already arisen
9. SYMPTOMS OF DIABETES
� increased urine output,
� excessive thirst,
� weight loss,
� hunger,
� fatigue,
� skin problems
� slow healing wounds,
� yeast infections, and
� tingling or numbness in the feet or toes.
10.
11. PLAN OF WORK
Data Collection / Data Analysis
& Literature Review
(3-4 months)
Data Warehouse Construction
(1-2 months)
Building Classifier
(3-3.5 months)
Experimental Result Analysis
(1-2 months)
Report Preparation
(1-1.5 months)
12. METHODOLOGY ADOPTED
Step 1- Preprocessing of data
Step 2-Dividing the patients into different group
Step 3- Apply the Fuzzy Inference
Step 4-Using Apriori Algorithms to find the relative pattern
Step 5- Build Classifier
14. PREPROCESSING
-- MERGING DATA FROM MULTIPLE SOURCES INTO
UNIQUE FORMAT
-- MISSING VALUE HANDLING
Use the attribute mean for all samples belonging to the
same as the given tuple.
15. DIVIDE THE DATA SET INTO 3 CLUSTER
Dividing the patients into 3 three different group
according to different condition of patients
� Very serious
� Serious
� Normal
16. FUZZY LOGIC
� Intuitionistic Fuzzy Set : claim that an
element x belongs to a given degree μA(x) to a fuzzy
set x should not belong to A to the extent 1-
μA(x)
α - Cut:
Let α be a number between 0 and 1. The α-cut of fuzzy
set A at level α is the set of those elements of A where
membership function is greater than or equal to α.
16
17. APRIORI ALGORITHM
We use apriori algorithm to find relative pattern
Suppose we get association rule
A→B confidence 95%
Means if patient has attribute A then it will has
attribute B also with confidence of 95 %
18. BUILD CLASSIFIER
� Various techniques may be applied –
✔ Multilayer Back propagation feed-forward ANN
-- Train ANN using the weight of attributes
calculate
from association rules confidence value.
✔ Simple Weighted Sum Method
� Expected Result :
� -- Class Label prediction of the patient as either:
� normal
� serious or
� very serious
✔
19. EXPECTED RESULT
� Class Label prediction of the patient as either:
� normal
� serious or
� very serious
o Association among various attributes with
respective confidence level (A🡪B , CL)
20. CONCLUSION
We are finding the relative pattern of patients in a
hospital we uses IFS, α-cuts, and Apriori algorithm for
discovering the knowledge of patients.
Our approach will successfully protect the patients’
personal data privacy and will achieve some gratifying
results from the experiments.
Certainly, the approach is not limited in a disease, it
can be used in other fields in the long run.
21. REFERENCES
• “Mining Cancer data with Discrete Particle Swarm Optimization and Rule
Pruning “
Yao Liu and Yuk Ying Chun
• “Identifying HotSpots in Lung Cancer Data Using Association Rule
Mining “
Ankit Agrawal and Alok Choudhary
• “Comparison of feature selection methods for multiclass cancer
classification based on microarray data” Xiaobo Li1,2*,
Sihua Peng3, Xiaosi Zhan1
• “Lung cancer statistics,” centers for Disease Control and Prevention,
URL:http://www.cdc.gov/cancer/lung/statistics
• en.wikipedia.org/wiki/World_Health_Organization
• www.whoindia.org
• A. Jemal, F. Bray, M.M. Center, J. Ferlay, E. Ward, D. Forman(2011).
"Global cancer tatistics". CA: a cancer journal forclinicians61
