Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

One of the major illnesses combating human races is Skin disease. Some skin diseases if not detected and treated early can result into cancer - a killer disease or disfigure the bearer. Discovery of these diseases frequently relies on the expertise of the medical professionals and skin biopsy results, in which sometimes the accuracy and prediction is deficient and as well is time consuming. Misdiagnosis is very rampart because these diseases always look alike, and could possibly be mistaken for each other. Therefore, there is need for a computer-based system for skin disease identification and classification through images to improve the diagnostic accuracy as well as to handle the scarcity of human experts. The current research sought to classify three selected skin diseases (Benign keratosis, Actinic keratosis and Dermatofibroma) that could disfigure or lead to cancer if proper diagnosis is not given. A convolutional neural network method designed upon tensor flow framework was used for the classification of the diseases. At the end of the implementation, results from the proposed system exhibits disease identification accuracy of 72% for Benign keratosis, 77% for Actinic keratosis and 69% for Dermatofibroma.

Skin Disease Classification using Deep Learning Methods

Skin disease rates have been increasing over the past few decades. It has led to both fatal and non-fatal disabilities all around the world, especially in those areas where medical resources are not good enough. Early diagnosis of skin diseases increases the chances of cure significantly. Therefore, this work is comparing six machine learning algorithms, namely KNN, random forest, neural network, naïve bayes, logistic regression, and SVM, for the prediction of the skin diseases. The information gain, gain ratio, gini decrease, chi-square, and relieff are used to rank the features. This work comprises the introduction, literature review, and proposed methodology parts. In this research paper, a new method of analyzing skin disease has been proposed in which six different data mining techniques are used to develop an ensemble method that integrates all the six data mining techniques as a single one. The ensemble method used on the dermatology dataset gives improved result with 94% accuracy in comparison to other classifier algorithms and hence is more effective in this area.

Prediction of Skin Diseases Using Machine Learning

Remote Skin Diseases Diagnosis System Using Machine Learning Techniques

https://www.imedpub.com/articles/recent-findings-in-computer-programming-and-frameworks.pdf

Recent Findings in Computer Programming and Frameworks

Skin diseases are reported to be the most common disease in humans among all age groups and a major cause of illness in sub-Saharan Africa. However, diagnosis and treatment of
skin disease are seen to be difficult, due to the orthodox approaches used by many medical centers globally. In recent times, artificial intelligence has been applied to enhance computer vision applications to permit easy detection of patterns in images. Notwithstanding this breakthrough in technology, the dermatological process in Ghana is yet to be automated, making the diagnosis of skin disease difficult and time-consuming. The current study sought to develop a web-based skin disease detection system (Medilab-Plus), which allows an online user to detect skin diseases in human and to make available, advises or possible medical actions in a precise short period. A convolutional neural network classifier built upon a Tensor flow framework for classifying a user-uploaded image as Eczema, Impetigo or Melanoma. Experimental results of the proposed system exhibit disease identification accuracy of 88% for Atopic dermatitis, 85% for Acne vulgaris and 84.7% for Scabies.

https://www.imedpub.com/articles/a-webbased-skin-disease-detection-system-medilabplus.pdf

A Web-Based Skin Disease Detection System: Medilab-Plus

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A Web-Based Skin Disease Detection System: Medilab-Plus