good informetion

1. CITY ENGINEERING COLLEGE
DEPARTMENT OF INFORMATION SCIENCE AND
ENGINEERING
MINI PROJECT PRESENTATION
ON
Multiple-Disease-Predictor using Python
By:
Akash kumar(1CE21IS002)
Anoop S N(1CE21IS003)
Spoorthi B(1CE21IS020)
Thanuja S (1CE21IS021)
Under the guidance of:
Prof.R.Mirudhula

2. ABSTRACT
 This study explores the application of machine learning algorithms for the
simultaneous detection of diabetes, heart disease, and Parkinson's disease
using Python.
 Leveraging a combination of classification techniques, including decision
trees, support vector machines, and neural networks, the system analyses
medical datasets to identify patterns and predict disease presence.
 The implementation focuses on optimizing model accuracy and efficiency,
demonstrating the potential of integrated disease detection to enhance early
diagnosis and treatment strategies.
 The results underscore the feasibility of employing machine learning in
multifaceted medical diagnostics.

3. INTRODUCTION
 Diabetes
 Type: Chronic condition
 Cause: High blood sugar levels due to body's inability to produce or use
insulin effectively
 Types:
 Type 1 Diabetes
 Type 2 Diabetes
 Gestational Diabetes
 Symptoms: Frequent urination, excessive thirst, weight loss, fatigue, blurred
vision
 Complications: Heart disease, kidney failure, nerve damage, eye problems

4. INTRODUCTION
 Heart Disease
 Type: Broad term encompassing various heart-related conditions
 Common Types:
 Coronary artery disease
 Heart arrhythmias
 Heart failure
 Congenital heart defects
 Causes: Atherosclerosis, high blood pressure, high cholesterol, smoking,
diabetes, sedentary lifestyle
 Symptoms: Chest pain, shortness of breath, palpitations, fatigue, swelling in
legs
 Complications: Heart attack, stroke, heart failure, sudden cardiac arrest

5. INTRODUCTION
 Parkinson's Disease
 Type: Neurodegenerative disorder
 Cause: Loss of dopamine-producing neurons in the brain
 Symptoms: Tremors, stiffness, slowness of movement, impaired balance,
speech changes
 Progression: Gradual worsening over time
 Complications: Difficulty swallowing, depression, cognitive impairment, sleep
disorders

6. SRS(SYSTEM REQUIREMENT
SPECIFICATION)
Operating System Database : Windows 11
Front End : HTML, CSS
Programming Language : Python
Web Browers : Any Web Browser
Required Application : VS Code
Required Framework : Flask

7. PROBLEM STATEMENT
 Objective: To explore the application of machine
learning for simultaneous detection of diabetes, heart
disease, and Parkinson's disease.
 Importance: Early diagnosis enhances treatment and
management of these diseases.

8. METHODOLOGY
 Data Collection
 Sources: Medical datasets for diabetes, heart disease, and Parkinson's
disease(Kaggle)
 Features: Patient demographics, clinical measurements, medical
history
• Data Preprocessing
 Data Cleaning: Remove missing or inconsistent data
 Feature Selection: Identify relevant features for each disease
 Data Normalization: Scale features to a standard range

9. METHODOLOGY
• Algorithms Used
 Support Vector Machines (SVM)
 Purpose: Classify data points by finding the optimal hyperplane
 Advantages: Effective in high-dimensional spaces, robust against
overfitting with appropriate kernel choice
• Logistic Regression
 Purpose: Predict the probability of disease presence based on
input features
 Advantages: Simple, interpretable, performs well with binary
classification

10. METHODOLOGY
• Model Training
 Procedure:
 Split data into training and testing sets
 Train models using training data
 Validate models using cross-validation techniques
• Model Evaluation
 Metrics:
 Accuracy
 Precision
 Recall
 F1 Score
• Comparison: Evaluate the performance of SVM and Logistic Regression
models on each disease dataset
• Implementation Tools
 Python Libraries:
 Scikit-learn
 Pandas
 NumPy

11. RESULT AND DISCUSSION
ON
TO
LIVE
DEMO

12. CONCLUSION
• Summary
 Objective Achieved: The study successfully applied machine learning
algorithms to detect diabetes, heart disease, and Parkinson's disease
simultaneously.
 Algorithms Used: Support Vector Machines (SVM) and Logistic Regression
were chosen for their effectiveness in classification tasks.
 Model Implementation: Python libraries such as Scikit-learn, Pandas, and
NumPy facilitated the development and evaluation of the models.

13. CONCLUSION
• Key Findings
 Effectiveness of SVM:
 SVM demonstrated high accuracy, particularly in cases with non-linear
relationships between features.
 Achieved an average accuracy of:
 Diabetes: 85%
 Heart Disease: 88%
 Parkinson's Disease: 90%
 Performance of Logistic Regression:
 Logistic Regression provided a good baseline performance with the
advantage of model interpretability.
 Achieved an average accuracy of:
 Diabetes: 80%
 Heart Disease: 83%
 Parkinson's Disease: 85%

14. CONCLUSION
• Implications
 Early Diagnosis: The high accuracy levels indicate that these machine learning
models can be effective tools for early diagnosis, potentially leading to better
patient outcomes through timely intervention.
 Integrated Disease Detection: The ability to detect multiple diseases
simultaneously underscores the potential of machine learning in creating
comprehensive diagnostic tools.
 Scalability: The methodology can be scaled and adapted to include additional
diseases, enhancing its utility in various medical contexts.

15. CONCLUSION
• Future Work
 Real-Time Data Integration: Implementing these models in a real-time data
environment to continuously monitor patient health metrics.
 Inclusion of More Diseases: Expanding the model to include other
diseases for broader diagnostic capabilities.
 Continuous Model Improvement: Regularly updating the models with new
data to improve accuracy and adapt to emerging medical knowledge.

16. CONCLUSION
• Conclusion
 Feasibility: The study confirms the feasibility of employing machine learning
in multifaceted medical diagnostics.
 Potential: Demonstrates the potential to significantly enhance early
diagnosis and treatment strategies, ultimately contributing to improved
healthcare outcomes.

17. REFERENCES
• Data Sources:
1. Diabetes Dataset: Kaggle - Pima Indians Diabetes Database
2. Heart Disease Dataset: Kaggle - Heart Disease UCI
3. Parkinson's Disease Dataset: Kaggle - Parkinson's Disease Dataset
• Machine Learning Libraries:
1. Scikit-learn: Pedregosa et al., "Scikit-learn: Machine Learning in Python",
Journal of Machine Learning Research, 2011.
2. Pandas: Wes McKinney, "Data Structures for Statistical Computing in
Python", Proceedings of the 9th Python in Science Conference, 2010.
3. NumPy: Travis Oliphant, "NumPy: A guide to NumPy", USA: Trelgol
Publishing, 2006.

18. THANK YOU