brain stroke ppt

1. BRAIN STROKE
SUBMITTED BY
NAME : S.RAJAYOGHA,
REG NO:9921146010
PROJECT GUIDE: DR. MARY DALLFIN BRUXELLA
DEPARTMENT : M.SC. DATA SCIENCE,
REVIEW:0TH REVIEW
REVIEW DATE:24.01.2023

2. ABSTRACT
BRAIN IS VERY COMPLEX AND INTERESTING ORGAN
A BRAIN STROKE ALSO CALLED AS A BRAIN ATTACK OR
CEREBROVASCULAR ACCIDENT(CVA) AND WORLDWIDE,
IT IS THE SECOND MAJOR REASON FOR DEATHS.
THE STROKE CAN OCCUR, WHEN SOME WASTE PARTICLE
RESTRICTS CIRCULATION OF BLOOD TO THE BRAIN. IF IT
BLOCKS, SUDDEN DEATH OR DAMAGES WILL OCCUR.
FIVE DIFFERENT ALGORITHMS ARE USED FOR COMPARISON
TO MADE A BETTER ACCURACY
THE AIM IS TO CREATE AN WEB APPLICATION WITH A USER-
FRIENDLY OPENING WHICH IS EASY TO USE AND HELP
PEOPLES ABOUT STROKE

3. OBJECTIVE
THE OBJECTIVE FOR THIS PROJECT IS TO CONSTRUCT A MODEL
FOR DETECTING STROKE USING MACHINE LEARNING
ALGORITHMS.
 “HEALTH CARE DATASET STROKE DATA” DATASET FROM
KAGGLE WEBSITE AND IT CONTAINING 202 RECORDS AND 12
PARAMETERS SUCH AS AGE, GENDER, WEIGHT, HEART DISEASE
ETC.

4. PROPOSED SYSTEM
• THE MAIN AIM OF THIS PROJECT IS TO BUILD AN EFFICIENT
PREDICTION MODEL AND DEPLOY FOR DETECTION OF
DISEASE.
• MACHINE LEARNING IS A FASTER-EMERGING TECHNOLOGY
OF ARTIFICIAL INTELLIGENCE THAT CONTRIBUTES VARIOUS
ALGORITHMS LIKE LOGISTIC REGRESSION, SVM, RANDOM
FORESTS AND MANY MORE WHICH IS EFFECTIVE IN
MAKING DECISIONS AND PREDICTIONS FROM THE LARGE
QUANTITY OF DATA PRODUCED BY THE HEALTHCARE
INDUSTRY.
• BASED ON THE PROPOSED PROBLEM, ML PROVIDES
DIFFERENT CLASSIFICATION ALGORITHMS TO DIVINE THE
PROBABILITY OF A PATIENT HAVING A BRAIN STROKE.

5. PROPOSED SYSTEM ADVANTAGE
THE PROPOSED STROKE SEVERITY PREDICTION AND IN-DEPTH
ANALYSIS SYSTEM HAS THE ADVANTAGES THEY ARE,
1.SYSTEM THAT AUTOMATICALLY CLASSIFIES AND ANALYSIS
STROKE SEVERITY INTO FOUR CLASSES USING NIHSS (NATIONAL
INSTITUTES OF HEALTH STROKE SCALE) FEATURES COLLECTED
IN REAL-TIME.
2. THE SYSTEM PROVIDES PATIENTS AND THEIR FAMILIES WITH
ALARM INFORMATION OF STROKE SEVERITY IN REAL-TIME, SO
PATIENTS CAN RECEIVE MEDICAL CENTER VISITS AND
EMERGENCY CARE.
3. LASTLY, DURING THE OPERATION OF AN ACTUAL SYSTEM, THE
PROPOSED MODEL USES ONLY 13 FEATURES OUT OF THE 18 NIHSS
FEATURES, INCLUDING AGE, SO THAT IT CAN PROVIDE FASTER
AND MORE ACCURATE SERVICE SUPPORT.

6. EXISTING SYSTEM
• IN RECENT TIMES, THE STRESS LEVELS IN
INDIVIDUALS ARE AT AN ALL TIME HIGH. THIS
INCREASES THE CHANCES OF STROKES IN
INDIVIDUALS.
• ABOUT 3.0 MILLION DEATHS RESULTED FROM
ISCHEMIC STROKE WHILE 3.3 MILLION DEATHS
RESULTED FROM HEMORRHAGIC STROKE. HENCE,
CORRECT DETECTION AND FINDING PRESENCE OF
STROKE INSIDE A HUMAN BECOMES ESSENTIAL.
• IN EXISTING SYSTEM, THERE ARE VARIOUS MEDICAL
INSTRUMENTS AVAILABLE IN THE MARKET FOR
PREDICTING BRAIN STROKE BUT THEY ARE VERY
MUCH EXPENSIVE AND THEY ARE NOT EFFICIENT
ENOUGH TO BE ABLE TO CALCULATE THE CHANCE
OF HAVING A BRAIN STROKE. .

7. DISADVANTAGE OF EXISTING SYSTEM
• BRAIN STROKE IS NOT ONLY THE MOST COMMON CASE OF
DEATH BUT ALSO A MAJOR CASE OF DISABILITY WITHOUT
PROPER TREATMENT.
• IT LEADS TO DEATH OR LONG TERM DISABILITY.
• DIAGNOSING BRAIN HEMORRHAGE USING IMAGE
SEGMENTATION ERROR WAS MINIMIZED.
• ACCURACY RATE WAS NOT DETERMINED BY USING
ALGORITHM.

8. CONCLUSION
• PREDICTIVE ANALYTICS IS A POPULAR BUSINESS
INTELLIGENCE TREND. THEY HELP DOCTORS MAKE
DATA DRIVEN DECISIONS IN NO TIME WHICH CAN
EVEN PREDICT AND PREVENT DEADLY DISEASES. IN
THIS PROJECT, WE HAVE CARRIED ON CATEGORICAL
FEATURE ANALYSIS, NUMERICAL FEATURE ANALYSIS
AND MULTI COLLINEARITY SUCCESSFULLY.

9. REFERENCES
• [1] T. I. SHOILY, T. ISLAM, S. JANNAT, S. A. TANNA, T. M. ALIF AND R. R. EMA,
"DETECTION OF STROKE DISEASE USING MACHINE LEARNING
ALGORITHMS," 2019 10TH INTERNATIONAL CONFERENCE ON
COMPUTING, COMMUNICATION AND NETWORKING TECHNOLOGIES
(ICCCNT), KANPUR, INDIA, 2019, PP. 1-6, DOI:
10.1109/ICCCNT45670.2019.8944689.
• [2] B. AKTER, A. RAJBONGSHI, S. SAZZAD, R. SHAKIL, J. BISWAS AND U.
SARA, "A MACHINE LEARNING APPROACH TO DETECT THE BRAIN
STROKE DISEASE," 2022 4TH INTERNATIONAL CONFERENCE ON SMART
SYSTEMS AND INVENTIVE TECHNOLOGY (ICSSIT), TIRUNELVELI, INDIA,
2022, PP. 897-901, DOI: 10.1109/ICSSIT53264.2022.9716345.
• [3] T. MALINI, M. DEEPALAKSHMI, B. DHIVYAA, P. KARTHIKESWARI AND N.
KAVIPRIYA, "ADVANCED STROKE DETECTION AND ALERT SYSTEM USING
MACHINE LEARNING," 2022 7TH INTERNATIONAL CONFERENCE ON
COMMUNICATION AND ELECTRONICS SYSTEMS (ICCES), COIMBATORE,
INDIA, 2022, PP. 1084-1089, DOI: 10.1109/ICCES54183.2022.9835761

10. THANK YOU