In the present day, there are many diseases which need to be identified at their early stages to start relevant treatments. If not, they could be uncurable and deadly. Due to this reason, there is a need of analysing complex medical data, medical reports, and medical images at a lesser time but with greater accuracy. There are even some instances where certain abnormalities cannot be directly recognized by humans. In healthcare for computational decision making, machine learning approaches are being used in these types of situations where a crucial data analysis needs to be performed on medical data to reveal hidden relationships or abnormalities which are not visible to humans. Implementing algorithms to perform such tasks itself is difficult, but what makes it even more challenging is to increase the accuracy of the algorithm while decreasing the required time for the algorithm to execute. In the early days, processing of large amount of medical data was an important task which resulted in machine learning being adapted in the biological domain. Since this happened, the biology and biomedical fields have been reaching higher levels by exploring more knowledge and identifying relationships which were never observed before. Reaching to its peak now the concern is being diverted towards treating patients not only based on the type of disease but also their genetics, which is known as precision medicine. Modifications in machine learning algorithms are being performed and tested daily to improve the performance of the algorithms in analysing and presenting more accurate information. In the healthcare field, starting from information extraction from medical documents until the prediction or diagnosis of a disease, machine learning has been involved. Medical imaging is a section that was greatly improved with the integration of machine learning algorithms to the field of computational biology. Nowadays, many disease diagnoses are being performed by medical image processing using machine learning algorithms. In addition, patient care, resource allocation, and research on treatments for various diseases are also being performed using machine learning-based computational decision making. Throughout this paper, various machine learning algorithms and approaches that are being used for decision making in the healthcare sector will be discussed along with the involvement of machine learning in healthcare applications in the current context. With the explored knowledge, it was evident that neural network-based deep learning methods have performed extremely well in the field of computational biology with the support of the high processing power of modern sophisticated computers and are being extensively applied because of their high predicting accuracy and reliability. When giving concern towards the big picture by combining the observations, it is noticeable that computational biology and biomedicine-based decision making in healthcare have now become dependent on machine learning algorithms, and thus they cannot be separated from the field of artificial intelligence.

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Involvement of Machine Learning Tools in Healthcare Decision Making.

Breast cancer is type of tumor that occurs in the tissues of the breast. It is most common type of cancer found in women around the world and it is among the leading causes of deaths in women. This article presents the comparative analysis of machine learning, deep learning and data mining techniques being used for the prediction of breast cancer. Many researchers have put their efforts on breast cancer diagnoses and prognoses, every technique has different accuracy rate and it varies for different situations, tools and datasets being used. Our main focus is to comparatively analyze different existing Machine Learning and Data Mining techniques in order to find out the most appropriate method that will support the large dataset with good accuracy of prediction. The main purpose of this review is to highlight all the previous studies of machine learning algorithms that are being used for breast cancer prediction and this article provides the all necessary information to the beginners who want to analyze the machine learning algorithms to gain the base of deep learning.

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Prediction of Breast Cancer, Comparative Review of Machine Learning Techniques, and Their Analysis

At the moment, the most prevalent form of cancer diagnosed in women across the globe is breast cancer. It develops in the breast tissue and is one of the most frequent causes of women’s death. This cancer can be cured if it is diagnosed at preliminary stage. Malignant and benign are two types of tumor found in case of breast cancer. Malignant tumors are deadly as their rate of growth is much higher than benign tumors. So, early identification of tumor type is pivotal for the appropriate treatment of a patient having breast cancer. In this work, Wisconsin Breast Cancer Dataset has been used which was collected from UCI repository. Our goal is to analyze the dataset and evaluate the performance of various machine learning algorithms for predicting breast cancer. Here, Support Vector Machine, Logistic Regression, K-Nearest Neighbors, Decision Tree, Naive Bayes and Random Forest classifiers have been implemented for classifying tumors into benign and malignant. The accuracy of each algorithm is calculated and compared to find the most suitable one. Based on the analysis, Random Forest and Support Vector Machine outperform other classifiers with accuracy of 96.5%. These classifiers can be used to build an automatic diagnostic system for preliminary diagnosis of breast cancer.

Malignant and Benign Breast Cancer Classification using Machine Learning Algorithms

In recent years, big data in health care is commonly used for the prediction of diseases. The most common cancer is breast cancer infections of metropolitan Indian women as well as in women worldwide with a broadly factor occurrence among nations and regions. According to WHO, among 14% of all cancer tumours in women breast cancer is well-known cancer in women in India also. Few researches have been done on breast cancer prediction on Big data. Big data is now triggering a revolution in healthcare, resulting in better and more optimized outcomes. Rapid technological advancements have increased data generation; EHR (Electronic Health Record) systems produce a massive amount of patient-level data. In the healthcare industry, applications of big data will help to improve outcomes. However, the traditional prediction models have less efficiency in terms of accuracy and error rate. This review article is about the comparative assessment of complex data mining, machine learning, deep learning models used for identifying breast cancer because accuracy rate of any particular algorithm depends on various factors such as implementation framework, datasets(small or large),types of dataset used(attribute based or image based)etc. Aim of this review article is to help to choose the appropriate breast cancer prediction techniques specifically in the Big data environment to produce effective and efficient result, Because “Early detection is the key to prevention-in case of any cancer”.

A Comprehensive Review Study on: Optimized Data Mining, Machine Learning and Deep Learning Techniques for Breast Cancer Prediction in Big Data Context

A common disease affecting millions of women in today’s world is breast cancer. To minimize the high number of superfluous breast biopsies, a few computer supported frameworks have been proposed somewhat recently. Machine learning provides potentially large opportunities for computer-aided diagnosis of a disease. Machine Learning gives possibly huge freedoms to computer helped finding of an illness. This paper investigates the informational indexes accessible to prepare the Machine Learning [ML] models and give a far reaching correlation of various kinds of models applied to anticipate breast cancer.

Detection of Breast Cancer using Machine Learning Algorithms – A Survey

Breast cancer is one of the most common and leading causes of cancer among women. Currently, it has become the common health issue, and its incidence has increased recently. Prior identification is the best way to manage breast cancer results. Computer-aided detection or diagnosis (CAD) systems plays a major role in prior identification of breast cancer and can be used for reduction of death rate among women. The main intention of this paper is to make use of the recent advances in the development of CAD systems and related techniques. The mainstay of the project is to predict whether the person is having breast cancer or not. Machine learning is nothing but training the machines to learn and perform by itself without any explicit program or instruction. So here, predicting whether a person is suffering with breast cancer or not is done with the help of the trained data.

Lakshmi Kavya Ravi

Papers

Breast Cancer Prediction via Machine Learning