Machine Learning in Medical Applications (original) (raw)

Machine Learning in Healthcare, Introduction and Real World Application Considerations

International Journal of Reliable and Quality E-Healthcare, 2018

Machine Learning, closely related to Artificial Intelligence and standing at the intersection of Computer Science and Mathematical Statistical Theory, comes in handy when the truth is hiding in a place that the human brain has no access to. Given any prediction or assessment problem, the more complicated this issue is, based on the difficulty of the human mind to understand the inherent causalities/patterns and apply conventional methods towards an acceptable solution, Machine Learning can find a fertile field of application. This article's purpose is to give a general non-technical definition of Machine Learning, provide a review of its latest implementations in the Healthcare domain and add to the ongoing discussion on this subject. It suggests the active involvement of entities beyond the already active academic community in the quest for solutions that “exploit” existing datasets and can be applied in the daily practice, embedded inside the software processes that are already in use.

Applications of Machine Learning in Medicine

IJIRIS:: AM Publications, 2023

As medical data and information technologies advance, an increasing number of practitioners are recognizing or planning to use artificial intelligence. Radically alter medical practice through the use of cutting-edge machine learning techniques. Research is now being done to determine how machine learning and predictive analysis might be used to tailor individual therapies. In order to create a medical model that can rapidly and reliably forecast new data, machine learning must first learn a large quantity of medical data and investigate the dependencies in data concentration. This allows for the early detection of diseases and the support of therapeutic decisions. Clinical medicine must continue to identify and treat severely ill emergency patients quickly while dealing with a relative paucity of medical resources. The age of big data has made clinical demands and thoughtful medical treatment generate demand. The solution to the fore mentioned challenges lies in the assistance supplied by machines.

Exploring the Possibilities of Using Machine Learning in Health Care

2021

In current days, professional trying to use machine learning in finding solutions of problems from almost every domain. The proposed study is conducted to check the feasibility of using machine learning in health care domain. We introduce the core concepts of machine learning with available types of algorithms. The study also consider the challenges that may encounter while integrating machine learning with clinical processes. A part of the study focusses on some of recent works on using machine learning as a solution of some health care problems. At the end, we discusses the future possibilities of machine learning in health care domain.

Burgeoning of Machine Learning in the field of Medical & Health Sciences

2021

In recent years, there has been a significant improvement in medical science and its related equipment, especially to diagnose a particular disease in the early stage. Use of technology helps in early diagnoses which leads to early treatment and recovery from disease. If the person does not receive proper treatment in accordance to the diagnosis the disease might get worse which results in increase morbidity and mortality rate. In short, early diagnoses and right treatment is the best remedy against any particular disease. Due to this fact, there is a need to analyze complex medical data, medical reports and medical images that could provide mechanisms to help the health care professional with more precision. In the field of medical science there is a need to devise standardized mechanism to analyze complex medical data. Introduction of machine learning and artificial intelligence provide ease to examine medical reports and images that aids healthcare professionals with greater accu...

The Future of Health care: Machine Learning

International Journal of Engineering & Technology

Machine learning (ML) is a rising field. Machine learning is to find patterns automatically and reason about data.ML enables personalized care called precision medicine. Machine learning methods have made advances in healthcare domain. This paper discuss about application of machine learning in health care. Machine learning will change health care within a few years. In future ML and AI will transform health care, but quality ML and AI decision support systems (DSS) Should Require to address the problems faced by patients and physicians in effective diagnosis.

Machine Learning Approaches to Automated Medical Decision Support Systems

This chapter provides an overview of the Machine Learning (ML) concepts in the clinical field which data may be collected, either by Health Care Professionals (HCP) or patients. These data may include activities and medication reminders, objective measurement of physiological parameters, feedback based on observed patterns, questionnaires and scores that require computational processes that give rise to useful information capable of supporting clinical decision making. The chapter describes ML in terms of learning concepts emphasizing the following approaches: supervised, unsupervised, semi-supervised, and reinforcement learning. The principles of concept classification are explained and the mathematical concepts of several methodologies are presented, such as neural networks and support vector machine among other techniques. Finally, a case study based on a radial basis function neural network aiming at the estimation of ECG waveform is presented. The proposed method reveals its suitability to support HCP on clinical decisions and practices.

Machine Learning Applied to Problem-Solving in Medical Applications

Computers, Materials & Continua, 2021

Physical health plays an important role in overall well-being of the human beings. It is the most observed dimension of health among others such as social, intellectual, emotional, spiritual and environmental dimensions. Due to exponential increase in the development of wireless communication techniques, Internet of Things (IoT) has effectively penetrated different aspects of human lives. Healthcare is one of the dynamic domains with ever-growing demands which can be met by IoT applications. IoT can be leveraged through several health service offerings such as remote health and monitoring services, aided living, personalized treatment, and so on. In this scenario, Deep Learning (DL) models are employed in proficient disease diagnosis. The current research work presents a new IoT-based physical health monitoring and management method using optimal Stacked Sparse Denoising Autoencoder (SSDA) technique i.e., OSSDA. The proposed model utilizes a set of IoT devices to collect the data from patients. Imbalanced class problem poses serious challenges during disease diagnosis process. So, the OSSDA model includes Synthetic Minority Over-Sampling Technique (SMOTE) to generate artificial minority class instances to balance the class distribution. Further, the hyperparameter settings of the OSSDA model exhibit heavy influence upon the classification performance of SSDA technique. The number of hidden layers, sparsity, and noise count are determined by Sailfish Optimizer (SFO). In order to validate the effectiveness and performance of the proposed OSSDA technique, a set of experiments was conducted on diabetes and heart disease datasets. The simulation results portrayed a proficient diagnostic outcome from OSSDA technique over other methods. The proposed method achieved the highest accuracy values i.e., 0.9604 and 0.9548 on the applied heart disease and diabetes datasets respectively.