ABSTRACT :

Cervical cancer is frequently a deadly disease, common in females. However, early diagnosis of cervical cancer can reduce the mortality rate and other associated complications. Cervical cancer risk factors can aid the early diagnosis. For better diagnosis accuracy, we proposed a study for early diagnosis of cervical cancer using reduced risk feature set and three ensemble-based classification techniques, i.e., extreme Gradient Boosting (XGBoost), AdaBoost, and Random Forest (RF) along with Firefly algorithm for optimization. Synthetic Minority Oversampling Technique (SMOTE) data sampling technique was used to alleviate the data imbalance problem. Cervical cancer Risk Factors data set, containing 32 risks factor and four targets (Hinselmann, Schiller, Cytology, and Biopsy), is used in the study. The four targets are the widely used diagnosis test for cervical cancer. The effectiveness of the proposed study is evaluated in terms of accuracy, sensitivity, specificity, positive predictive accuracy (PPA), and negative predictive accuracy (NPA). Moreover, Firefly features selection technique was used to achieve better results with the reduced number of features. Experimental results reveal the significance of the proposed model and achieved the highest outcome for Hinselmann test when compared with other three diagnostic tests. Furthermore, the reduction in the number of features has enhanced the outcomes. Additionally, the performance of the proposed models is noticeable in terms of accuracy when compared with other benchmark studies for cervical cancer diagnosis using reduced risk factors data set.

EXICITING SYSTEM :

 Dataset Description :

cervical cancer risk factors data set used in the study was collected at “Hospital Universitario de Caracas” in Caracas, Venezuela and is available on the UCI Machine Learning repository . It consists of 858 records, with some missing values, as several patients did not answer some of the questions due to privacy concerns. the data set contains 32 risk factors and 4 targets, i.e., the diagnosis tests used for cervical cancer. It contains different categories of feature set such as habits, demographic information, history, and Genomic medical records. Features such as age, Dx: Cancer, Dx: CIN, Dx: HPV, and Dx features contains no missing values. Dx: CIN is a change in the walls of cervix and is commonly due to HPV infection; sometimes, it may lead to cancer if it is not treated properly. However, Dx: cancer variable is represented if the patient has other types of cancer or not. Sometimes, a patient may have more than the cervical cancer risk factors data set used in the study was collected at “Hospital Universitario de Caracas” in Caracas, Venezuela and is available on the UCI Machine Learning repository . It consists of 858 records, with some missing values, as several patients did not answer some of the questions due to privacy concerns. the data set contains 32 risk factors and 4 targets, i.e., the diagnosis tests used for cervical cancer. It contains different categories of feature set such as habits, demographic information, history, and Genomic medical records. Features such as age, Dx: Cancer, Dx: CIN, Dx: HPV, and Dx features contains no missing values. Dx: CIN is a change in the walls of cervix and is commonly due to HPV infection; sometimes, it may lead to cancer if it is not treated properly. However, Dx: cancer variable is represented if the patient has other types of cancer or not. Sometimes, a patient may have more than one type of cancer. In the data set, some of the patients do not have cervical cancer, but they had the Dx: cancer value true. +erefore, it is not used as a target variable. he data set, some of the patients do not have cervical cancer, but they had the Dx: cancer value true. +erefore, it is not used as a target variable.

Table 1 presents a brief description of each feature with the type. Cervical cancer diagnosis usually requires several tests; this data contains the widely used diagnosis tests as the target. Hinselmann, Schiller, Cytology, and Biopsy are four widely used diagnosis tests for cervical cancer. Hinselmann or Colposcopy is a test that examines the inside of the vagina and cervix using a tool that magnifies the tissues to detect any anomalies . Schiller is a test in which a chemical substance called iodine is applied to the cervix, where it stains healthy cells into brown colour and leaves the abnormal cells uncoloured, while cytology is a test that examines body cells from uterine cervix for any cancerous cells or other diseases. And Biopsy refers to the test where a small part of cervical tissue is examined under a microscope. Most Biopsy tests can make significant diagnosis.

Dataset Preprocessing :

the data set suffers from a huge number of missing values; 24 features out of the 32 contained missing values. Initially, the features with the huge percentage of missing values were removed. STDs: Time since first diagnosis and STDs: Time since last diagnosis features were removed since they have 787 missing values (see Table 2), which is more than half of the data. However, the data imputation was performed for the features with fewer numbers of missing values. +e most frequent value technique was used to impute the remaining missing values. Additionally, the data set also suffers from huge class imbalance. the data set target labels were imbalanced with 35 for the Hinselmann, 74 for Schiller, 44 for Cytology, and 55 Biopsy out of the 858 records as shown in Figure 1. SMOTE was used to deal with class imbalance. SMOTE works by oversampling the minority class by generating new synthetic data for minority instances based on nearest neighbours using the Euclidean Distance between data points . Figure 1 shows the number of records per class labels in the data set.

DISADVANTAGES OF EXISTING SYSTEM :

1) Less accuracy

2)low Efficiency

PROPOSED SYSTEM :

the model was implemented in Python language 3.8.0 release using Jupyter Notebook environment. Ski-learn library was used for the classifiers along with other needed built-in tools, while separate library (xgboost 1.2.0) was used for XGBoost ensemble. +ere is K-fold cross validation with K =10 for partitioning the data into training and testing. Five evaluation measures such as accuracy, sensitivity (recall), specificity (precision), positive predictive accuracy (PPA), and negative predictive accuracy (NPA) were used. Sensitivity and specificity are focused more during the study due to the application of the proposed model. Accuracy denotes the percentage of correctly classified cases, sensitivity measures the percentage of positives cases that were classified as positives, and specificity refers to the percentage of negative cases that were classified as negatives. Moreover, the criteria for the selection of the performance evaluating. measures depend upon the measures used in the benchmark studies. Two sets of experiments were conducted for each target using selected features by using Firefly feature selection algorithm and 30 features for four targets. +e SMOTE technique was applied to generate synthetic data.

SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10

HARDWARE REQUIREMENTS:

 Processor : Core I3
 RAM Capacity : 2 GB
 Hard Disk : 250 GB
 Monitor : 15″ Color
 Mouse : 2 or 3 Button Mouse
 Key Board : Windows 08/10