Cancer is one of the most deadly diseases in the world and it is a subject of concern because until till date they cannot found the real treatment for this disease. If and only if this disease is detected in early stage, patients having this disease can be saved. If it is detected in latter stage then chance of survival will be very less. Because of this, early and true diagnosis is an important issue and plays a key role to cure this disease. In the work, Gaussian Naive Bayes algorithm is used for classification cancer. The algorithm is tested by applying it on two datasets in which the first is Wisconsin Breast Cancer dataset (WBCD) and the second is lung cancer dataset. The evaluation results of the proposed algorithm have achieved 98% accuracy of predicting breast cancer and 90% of predicting lung cancer.

Cancer Classification Using Gaussian Naive Bayes Algorithm

This paper aims at the requirement for an interactive learning framework which empowers the successful checking of disorder in a patient. Principal component analysis stands out as an outstanding algorithm to significantly classify the target classes. PCA blends associated characteristics and makes a dissipated showcase of its components well. Scree plot examination gives solidarity of how many principal components are to be retained. Support Vector Machines (SVM ) is a fast and dependable classification algorithm that outperforms other techniques with a limited amount of data. The obtained components will be served to Support Vector Machine for further classification. The pre-dangerous stage will remind the clinical experts to give additional consideration to those patients. The expectation ability is estimated in terms of the confusion matrix. The model developed gives a high and uncompromising accuracy in early detection of different levels of malignancy

Efficient multi-level lung cancer prediction model using support vector machine classifier

One of the deadly diseases prevailing worldwide is cancer. The rigorous symptoms of cancers should be studied properly prior to the diagnosis to save patients life. Thus, an automatic prediction sy...

Rider-chicken optimization dependent recurrent neural network for cancer detection and classification using gene expression data

Huge amounts of data are handled regularly by the medical field The findings can be influenced by managing enormous information using conventional methods The levels in expression of thousands of genes that can be calculated concurrently in today's biology research in a single experiment scientist Machine learning algorithms can be used in medical research to find out information, particularly for prediction of disease with the help of cell's molecular level is reflected in the profile of gene expression For order assessing patient medications and physicians, early identification of illness is key Machine learning algorithms like Decision trees, Support vector machine, Artificial Neural Networks, Bayes classification techniques, k-Nearest Neighbors classification techniques, etc are used to determine different diseases With high accuracy, the use of machine learning algorithms can lead to rapid prediction of disease Because of this DNA micro-array method, it is possible to track large numbers of gene expressions Using this large amount of gene data, researchers try to find the probability of diagnosis of disease with data-set gene expression A number of techniques with comfortable results are developed

Soft Computing based Approaches for Classifying Diseases using Medical Diagnosis Dataset

Deep learning has been paid great attention in several fields for the good reason, and their results achieved were not before possible. In deep learning, the models are trained based on huge labelled dataset and the neural network architectures consist of several layers. Still, the methods suffer to achieve expected higher performance and introduce poor classification accuracy. Towards improving the classification performance, this paper introduces the deep learning networks for the cancer classification application. The main aim is to develop the optimized model for classification application. Accordingly, the input data is pre-processed using Log transformation for converting the data to its uniform value range. Then, the feature selection is done based on Wrapper approach to select the important features for classifying the cancer. Once the features are selected, the cancer classification is performed using Deep Recurrent Neural Network (Deep RNN), which is trained by the proposed Fractional-Atom Search Algorithm (Fractional-ASO). The Fractional-ASO is designed by integrating Fractional Calculus with Atom Search Optimization (ASO). The performance of the Fractional-ASO-based Deep RNN is evaluated in terms of accuracy, True Positive Rate (TPR) and True Negative Rate (TNR). The proposed Fractional-ASO -based Deep RNN method achieves the maximal accuracy of 92.87%, maximal TPR of 92.87%, and the maximal TNR of 93.48% using colon dataset.

Fractional-atom search algorithm-based deep recurrent neural network for cancer classification

In the present epoch of technology medical field has become one of the favorite topics of researcher and cancer is among one of them. It is a topic of concern because actual treatment of this disease is not found till date. Patients having this disease can only be saved if and only if it is found in early stage (stage I and stage II). If it is detected in latter stage (stage III and stage IV) then chance of survival will be very less. Machine learning and data mining technique will help medical field to tackle with this problem. Cancer has various symptoms such as tumor, abnormal bleeding, more weight loss etc. It is not necessary that all kinds of tumors are cancerous. Tumors are basically of two types i.e. benign or malignant. To provide appropriate treatment to the patients, symptoms must be studied properly and an automatic prediction system is required which will classify the tumor into benign or malignant. In today's internet world, bulk of data is generated on social media or healthcare websites. From this huge amount of data, symptoms can be fetched by using data mining technique, which will be further useful for cancer detection or classification. This paper makes survey of such latest research study that makes use of online and offline data for cancer classification.

Shilpi Shandilya

Papers

Survey on recent cancer classification systems for cancer diagnosis