An Enhanced Ensemble Diagnosis of Cervical Cancer A Pursuit of Machine Intelligence Towards Sustainable Health

Project Code :TCMAPY507

Objective

The main objective of this application is to classify the cervical cancer disease(i.e. Whether the person is having cancer disease or not) using machine learning algorithms.

Abstract

Cervical cancer is a potentially life-threatening disease marked by health practitioners. The late diagnosis and treatment, being quite challenging, stake the precious lives of patients. In both developed and undeveloped states, the formal screening for disease identification suffers due to its medical cost, unavailable health facilities, society norms, and late appearance of symptoms. Machine intelligence is cost-effective, computationally inexpensive, and early diagnosis of several types of diseases, including cervical cancer. The patients are not required to pass through contemporary and tedious medical procedures, and early diagnosis of cervical cancer is quite handy with machine-intelligent solutions. The problem with the current machine classification methods for disease identification is the reliance on a single classifier’s prediction accuracy. The adoption of single classification methods doesn’t ensure the optimum prediction due to bias, over-fitting, mishandling of noisy data, and outliers. This research study proposes an Ensemble classification method based on majority voting for an accurate diagnosis addressing the patient’s medical conditions or symptoms. The study experiments a wide range of available classifiers, namely Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Naive Bayes (NB), Multiple Perceptron (MP and Logistic Regression (LR) classifiers. The study records a significant enhancement in prediction accuracy of 94% that outperforms the prediction accuracies of single classification methods tested on the same bench marked datasets. Thus, the proposed model bestows a second opinion to health practitioners for disease identification and timely treatment.


KEYWORDS: Machine Learning Algorithms, Classifiers, Cervical Cancer, Ensemble Classification.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

SYSTEM SPECIFICATIONS:

SOFTWARE CONFIGURATIONS:

Technology                    :  Deep Learning, Python

Libraries using               :  Pandas, Matplotlib, NumPy, Scikit-learn

Version                           :  Python 3.6

Platform                         : Stream lit

HARDWARE CONFIGURATIONS:

RAM                               :  8GB, 64 bit os.

Processor                         :  I3/I5 Intel processor

Operating system            :  Windows 10 pro

Learning Outcomes

LEARNING OUTCOMES:

·         Objective of the project .

·         How Internet Works.

·         What is a  search engine  and how browser can work.

·         What type of technology versions are used .

·         Working Procedure.

·         Introduction to basic technologies used for.

·         How project works.

·         Input and Output modules .

·         Frame work use.

·         About python.

·         About streamlit

·         What are Deep learning algorithms.

·         How can we identify and detect the fake reviews by using machine learning algorithms.

·         What is meant by preprocessing.

·         What are preprocessing techniques.

·         How can we collect dataset.

·         Practical exposure to

·         Hardware and software tools.

·         Solution providing for real time problems.

·         Working with team/ individual.

·         Work on Creative ideas.

Demo Video

mail-banner
call-banner
contact-banner
Request Video

Related Projects

Final year projects