MATHS: Machine Learning Techniques in Healthcare System (original) (raw)
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Smart Healthcare Prediction System Using Machine Learning
In this paper, we have introduced the techniques and applications of machine learning in the healthcare system. We know that day by day large amount of data is generating in healthcare industry and other industries as well. Such large amount of data cannot be processed by humans manually in a short time to make diagnosis of diseases and treatments. To reduce this manual work, we have explored data management techniques and machine learning algorithms in healthcare applications to develop accurate decisions. It also gives the detailed description of medical data which improves various aspects of healthcare applications. It is the latest powerful technology that will reduce the manual work of professionals. In this paper, we will be using the Naïve Bayes machine learning algorithm to train our machine to predict the different types of diseases. It uses existing medical information in various databases to rework it into new results and researches. It will extract the new patterns from large datasets to make prediction and knowledge associated with these patterns. Particularly, the important task is to get data by means of automatic or semi-automatic.
A Machine-Learning-based Predictive Smart Healthcare System
2025
Background and Objectives: In smart grid paradigm, there exist many versatile applications to be fostered such as smart home, smart buildings, smart hospitals, and so on. Smart hospitals, wherein patients are the possible consumers, are one of the recent interests within this paradigm. The Internet of Things (IoT) technology has provided a unique platform for healthcare system realization through which the patients' health-based data is provided and analyzed to launch a continuous patient monitoring and; hence, greatly improving healthcare systems. Methods: Predictive machine learning techniques are fostered to classify health conditions of individuals. The patients' data is provided from IoT devices and electrocardiogram (ECG) data. Then, efficient data pre-processings are conducted, including data cleaning, feature engineering, ECG signal processing, and class balancing. Artificial intelligence (AI) is deployed to provide a system to learn and automate processes. Five machine learning algorithms, including Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), logistic regression, Naive Bayes, and random forest, as the AI engines, are considered to classify health status based on biometric and ECG data. Then, the output would be the most proper signals propagated to doctors' and nurses' receivers in regard of the patients providing them by initial pre-judgments for final decisions. Results: Through the conducted analysis, it is shown that logistic regression outperforms the other AI machine learning algorithms with an F1 score, recall, precision, and accuracy of 0.91, followed by XGBoost with 0.88 across all metrics. SVM and Naive Bayes both achieved 0.85 accuracy, while random forest attained 0.86. Moreover, the Receiver Operating Characteristic Area Under Curve (ROC-AUC) scores confirm the robustness of Logistic Regression and XGBoost as apt candidates in learning the developed healthcare system. Conclusion: The conducted study concludes a promising potential of AI-based machine learning algorithms in devising predictive healthcare systems capable of initial diagnosis and preliminary decision makings to be relied upon by the clinician. What is more, the availability of biometric data and the features of the proposed system significantly contributed to primary care assessments.
Towards for Designing Intelligent Health Care System Based on Machine Learning
Iraqi Journal for Electrical and Electronic Engineering, 2021
Health Information Technology (HIT) provides many opportunities for transforming and improving health care systems. HIT enhances the quality of health care delivery, reduces medical errors, increases patient safety, facilitates care coordination, monitors the updated data over time, improves clinical outcomes, and strengthens the interaction between patients and health care providers. Living in modern large cities has a significant negative impact on people’s health, for instance, the increased risk of chronic diseases such as diabetes. According to the rising morbidity in the last decade, the number of patients with diabetes worldwide will exceed 642 million in 2040, meaning that one in every ten adults will be affected. All the previous research on diabetes mellitus indicates that early diagnoses can reduce death rates and overcome many problems. In this regard, machine learning (ML) techniques show promising results in using medical data to predict diabetes at an early stage to s...
Disease Prediction Application Using Machine Learning
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
The health care systems collect data and reports from the hospitals or patient's database by machine learning and data processing techniques which is employed to predict the disease so as to create reports supported the results which used for various kinds of predictions for disease and which is that the leading explanation for the human's death since past years. Medical reports and data had been extracted from various databases to predict a number of the required diseases which are commonly found in people nowadays breast cancer, heart disease and diabetes disease and make their life more critical to measure. Nowadays technology advancement within the health care industry has been helping people to create their process easier by suggesting hospitals and doctors to travel to for his or her treatment, where to admit and which hospitals are the simplest for the treating the desired disease. we've implemented this sort of system in our application to form people's life simpler by predicting the disease by inputting certain data from their reports which can give the result positive or negative supported the disease prediction they are going to be having a choice to get recommendation of best hospitals with best doctors nearby from the past users or guardians.
Application of Machine Learning in Disease Prediction
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023
Healthcare is a sector that is always changing. Healthcare professionals may find it challenging to stay current with the constant development of new technologies and treatments. As a result, the purpose of this research paper is to try and implement machine learning features in a specific system for health facilities. Knowing if we are ill at an early stage rather than finding out later is crucial. The entire process of treatment can be made much more effective if the disease is predicted ahead using specific machine learning algorithms as opposed to directly treating the patient. In this work, disease is predicted based on symptoms using machine learning. Machine learning algorithms like Naive Bayes, Decision Tree, Random Forest, and KNN are used to forecast the disease on the provided dataset. As you can see, there are numerous potential applications of machine learning in clinical care in the areas of patient data improvement, diagnosis, and treatment, cost reduction, and improved patient safety.
E-Health System for Disease Prediction based on Machine Learning
The health reports of the persons including diagnostics information and medical prescriptions are provided in the form of test based case notes due to this the previous health conditions and the medicines used by the person are not known when he visits the hospital later. But storing all the health information of a person in the cloud as the soft copy reduces this problem. To achieve this each and every hospital, dispensary, labs must have an internet connection for registration of patient's data, each patient will be identified by the unique Health Id and all the data of the patient will be stored in the cloud and the data can be accessed by only the particular patient. In order to prevent and treat illness, it is critical to perform an accurate and timely analysis on any health-related problem. The ability to diagnose disease by obtaining all information from a linked Health ID combined with Machine Learning techniques will improve the system's ability to detect diseases. We believe that our diagnostic model can operate like a doctor in the earlier diagnosis of this disease, allowing for timely treatment and the preservation of life.
Machine Learning in Healthcare Industry: Tools and Techniques
From last two decades, technology evolving very quickly and changed the view nearly all aspects of modern life. There is a lot of development made in the area of machine learning. It is the subset of Artificial Intelligence that gives machine the possibility to determine and make decision without using explicit instructions. Today, ML has a tremendous impact in across all over the world and benefited various areas like medical diagnostics, fraud detection, driverless vehicles and security surveillance etc. Machine learning layout an inspiring set of technologies that consist of pragmatic tools for reviewing data and making predictions with the latest headway in artificial intelligence. The major theme of the paper is focused on the various tools and techniques of machine learning and its application in healthcare industry.
Disease Prediction using Machine Learning
international journal for research in applied science and engineering technology ijraset, 2020
Machine learning has various applications and one of them is healthcare. There should be much more advanced medical facilities so as to provide the best possible treatment for the patients[3].Also there are many machine learning algorithms (such as KNN, Random Forest and Decision Tree Classifier algorithms and many more) which were selected and on the given data many algorithms were applied so as to produce the best results. We can say that when machine learning implemented in healthcare can lead to a high increase in patient satisfaction. so this research paper, will try to implement functions of machine learning in health facilities in a particular system[8]. Instead of directly performing treatment for the patient, if the disease is predicted beforehand using certain machine learning algorithms then the entire process of treatment can be made much more efficient[12]. There are also some cases which occur when early diagnosis of a disease is not performed or carried out. Hence disease prediction is a really important step while treating the patient. As it is said "Prevention is better than cure", the right prediction of disease would definitely lead to an early prevention of that particular disease[19].
Medical treatment processes around the world are expected to revolutionize with the help of AI-aided healthcare services. AI can replicate human cognition and is capable of learning, reasoning, making decisions, and acting. The adaptation of AI can radically reshape the entire healthcare businesses. This paper proposes a comparative analysis of four classification algorithms. The selected algorithms are k-Nearest Neighbour, Naive Bayes, Decision Tree, and Random Forest which predict some commonly identified ailments. These Supervised Machine Learning classifiers are used on a disease prediction data-set to predict 41 prevalent diseases based on any 5 conspicuous symptoms from the data set’s 132 common symptoms. After the study we conclude, Random Forest had the highest accuracy of 99.5%, followed by Decision Tress at 95.8%, then K Nearest Neighbor at 93.4%, and lastly Naive Bayes at 87.7%. Our experiment achieved higher accuracy than previous investigation dealing with identical cla...
Advanced Patient Monitoring System with Diseases Prediction System using Machine Learning
Middle East Journal of Applied Science & Technology, 2022
IoT and machine learning (ML) are becoming increasingly efficient in the medical and telemedicine areas all around the world. This article describes a system that employs latest technology to give a more accurate method of forecasting disease. This technology uses sensors to collect data from the body of the patient. The obtained sensor information is collected with NodeMcU before being transferred to the Cloud Platform "ThinkSpeak" through an ESP8266 Wi-Fi module. ThinkSpeak is a cloud server that provides real-time data streams in the cloud. For the best results, data currently saved in the cloud is evaluated by one of the machine learning algorithms, the KNN algorithm. Based on the findings of the analysis and compared with the data sets, the disease is predicted and a prescription for the relevant disease is issued.