Mini Review - (2025) Volume 11, Issue 1
One major implication of the establishment and exponential growth of the data analytics which brought computing to the lay people worldwide which that the ordinary people started creating all types of transactions and content that generate new data.
Data analytics has developed in addition of business and government institutional electronically recovered every transaction of each customer vendor and supplier and thus they have been accumulating data in so called the data analytics.
Data analytics is everywhere so every vector of the economist stands to benefit by harnessing it approximately with technology that will help data users and managers make better decisions test on the historical evidence.
Data analytics is concerned with reporting events and analysing the data that led to them. Determining the cause while keeping an eye on fresh data to learn what is going currently, data analytics uses practical tests and methodologies to create predictions about the future.
Today, analytics are crucial for gaining a competitive edge in every company, and he provides the evidence.
How can any corporation create a large data advanced analytics economic system to find new business opportunities? Data analytics services today go beyond the fundamental ideas.
Multi detecting object tracking • R-CNN • Deep learning • Traffic detection • Machine learning • Image classification
Analytics in the healthcare industry refers to the use of enormous volumes of data to give businesses information. These understanding, which has been supported fact-based decision-making, which are created through analytical-disciplines. These choices enhance planning, management, measurement, and learning as a result [1].
If the US healthcare could use big data creatively and effectively to efficiency and quality of waste that the potential value from data in the sector could be more than $300 billion value every year.
Data analytics in healthcare also has a huge amount of benefits and even life-saving effects. Big-style data, in its simplest form, is the enormous amounts of data generated by the digitization of everything that is then collected and processed by a particular technology. When used in healthcare, it will make use of specific health information about a population (or about a specific person) and could assist to stop epidemics, treating diseases, and saving expenses.
In fact, healthcare analytics holds the promise of lowering medical expenses, predicting epidemic outbreaks, preventing diseases, and generally enhancing the quality of life. The world's population is living longer on average, which presents significant difficulties for current treatment delivery strategies. Similar to commercial entrepreneurs, health professionals are capable of gathering enormous volumes of data and seeking the most effective ways to use these figures [2].
Data analytics is playing a key role in the healthcare system where it analyses the raw data provided by the patients the provided raw data is analyzed into records to draw a meaningful action which are used to inform and drive smart decisions in the business world the healthcare industry generates a bid-business amount of data but struggles to convert the data into insight that improve patient outcome and operational efficiency.
Data analytics in healthcare aims to assist overcome barriers to the widespread deployment of data-driven intelligence by making it easier to exchange data with coworkers and outside partners and to visualize it for general public consumption.
To help healthcare providers respond more rapidly and change the environment and technology for healthcare data analytics, it is crucial to deliver the most up-to-date and accurate data-driven forecast possible in real-time.
Data analytics and healthcare can be applied to important aspects of patient care and management of the operation the analysis introduces methods of developing and provision of clinical care to decrease the disease rate and prevention from diseases counting the occasion effectiveness and increasing the rate of various treatment options the capacity of data analytics to convert the raw healthcare data into developing intelligence is expected to have the greatest impact on which cars of healthcare [3].
• Finding an expectation of diseases.
• Hotel administrative procedures are automated.
• Early illness prediction.
• Reducing the frequency of unnecessary doctor appointment.
• They improved health insurance rate calculation.
• Personalization of healthcare through more efficient patient data sharing.
In our present situation of worldwide there are many people who are suffering with various diseases which are caused dew to various types of deficiencies and lack of various vitamins. In according to the paper-based survey which was conducted for nearly 1000 patients from different countries at the end of the survey it was concluded with from different countries there are many diseases with different age groups.
Now a day every researcher is working on most unique methodology by which we can just scan by using the mobile phone. From collected dataset from university of US which created framework from which data was collected through phone calls and messages. There are more than 300 text messages or calls. At the end report was completely filled with vitamin deficiency.
In data analytics algorithm with a name artificial neural network which is completely based on three-layer architecture. This architecture consists different layers as input layer, a hidden layer, and output layer. The input layer is the layer where data is inputted into the network. The hidden layer is the layer that is responsible for performing the computations to process the data. The output layer is the layer responsible for producing the results. In order for the ANN to learn, it must be trained with data. During the training phase, the weights and biases of the neurons in the hidden layer are adjusted to optimize the results.
The first step in treating a person with a mental health disorder is to seek help from a qualified mental health professional. This professional will assess the individual’s symptoms and needs and develop a treatment plan that is tailored to the individual. Treatment options may include talk therapy, medication, lifestyle changes, and support groups. It is important to ensure that the person with the disorder is involved in the decision-making process and that their needs and preferences are respected. Additionally, it is important to provide family and friends with education and support to help them understand and effectively help the individual with the disorder [4].
Healthcare in data analytics
The healthcare system architecture is composed of three main layers: The technology layer, the data layer, and the service layer.
The technology layer is the underlying infrastructure that makes the healthcare system possible. This includes hardware components such as computers, servers, and storage devices, as well as software such as operating systems, databases, and application software. This layer also includes the network connections that enable communication between the different components in the healthcare system.
The data layer is the layer where data is stored and maintained. This includes both structured and unstructured data, such as patient records, medical images, and laboratory reports. Data is stored in databases and is accessible to authorized personnel.
The service layer is the top layer of the architecture and is the layer that provides users with access to the healthcare system. This includes web portals, mobile applications, and other user interfaces. This layer also provides the necessary security measures to protect patient data and ensure that only authorized individuals can access it.
Cloud computing: Cloud computing is an increasingly popular technology that is being used to help organizations manage their big data analytics. Cloud computing provides a platform for storing and processing large amounts of data, allowing for faster and more efficient analysis. It can be used to store and process large datasets, such as those generated by social media analytics, machine learning, and other big data applications. By leveraging the power of the cloud, organizations can quickly analyze large amounts of data and make more informed decisions. Additionally, cloud computing can help organizations reduce their costs associated with storing and processing data.
Internet of Things (IoT): The Internet of Things (IoT) has become a major driver of big data analytics. IoT enables the collection of huge amounts of data from connected devices and sensors, which can then be analyzed to gain deeper insights into customer behavior, product performance, and more. By leveraging big data analytics, companies can gain a better understanding of their customers, optimize their operations, and gain a competitive edge. Additionally, IoT-generated data can be used to create predictive models that can be used to identify potential problems before they occur. IoT also has the potential to make data more accessible and increase the speed of data analysis.
In conclusion data analytics is playing a key role in the healthcare system where it analyses the raw data provided by the patients the provided raw data is analyzed into records to draw a meaningful action which are used to inform and drive smart decisions in the business world the healthcare industry generates a bid-business amount of data but struggles to convert the data into insight that improve patient outcome and operational efficiency. Data analytics in healthcare aims to assist overcome barriers to the widespread deployment of data-driven intelligence by making it easier to exchange data with coworkers and outside partners and to visualize it for general public consumption.
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Citation: Lakshmi MV, et al. "Big Data Analytics in Healthcare". Health Econ Outcome Res, 2025, 11(1), 1-2.
Received: 23-Sep-2023, Manuscript No. HEOR-23-114593; Editor assigned: 26-Sep-2023, Pre QC No. HEOR-23-114593 (PQ); Reviewed: 10-Oct-2023, QC No. HEOR-23-114593; Revised: 20-Jan-2025, Manuscript No. HEOR-23-114593 (R); Published: 27-Jan-2025, DOI: 10.35248/2471-268X.24.11(1).001
Copyright: © 2025 Lakshmi MV, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
