Using the ML approach, we can now assess diabetes in the patient. Learn more about how the algorithms used are dramatically changing health care.
Diabetes is one of the deadliest diseases in the world. It is not only a disease, but also a creator of a variety of diseases such as heart attacks, blindness, and kidney diseases.
The usual detection process is that patients visit the diagnostic center, consult their physician, and sit tight for a day or more to get their reports. Also, every time they want to get their diagnosis report, they have to waste their money.
With the rise of machine learning approaches, we have the potential to find a solution to this problem and have developed a system using data mining that has the potential to tell whether a patient has diabetes. Furthermore, the preoperative tingling of the disease leads to the treatment of patients. Data mining has the potential to extract large amounts of hidden knowledge from diabetes-related data.
For that reason, it has an important role in diabetes research, now more than ever. The goal of this research is to develop a system that can measure the patient’s diabetic risk level with high accuracy. This research focuses on developing a system based on three
Classification methods: Decision Tree, Nav Bayes, and Support Vector Machine Algorithms.
Currently, the models give 84.6667%, 76.6667%, and 77.3333% accuracy to the Decision Tree, Nav Bayes, and SMO Support Vector machines, respectively. These results are validated using the receiver sensitively operating characteristic curves.
The developed ensemble method uses the votes given by other algorithms to give the final result. This voting system eliminates algorithm-based false classifications. This helps to get a more accurate estimate of the disease. We used the Data Mining extension for data preprocessing and experimental analysis. The results of a significant improvement in the accuracy of the ensemble method are compared with other existing methods.
Methodology
These algorithms do not work alone; we have developed an ensemble method that uses the votes given by other algorithms to give the final result. The system accepts the result, only when more than two models give the same predicted results.
It gives the decision of the majority. This voting system eliminates algorithm-based misclassifications. This helps to get a more accurate estimate of the disease.
The decision tree is the J48 algorithm
Decision-tree is a tree structure that has the appearance of a flowchart. It can be used as a method for classification and estimation with representation using nodes and internodes.
The root and internal nodes are test cases. Leaf nodes are treated as class variables. To classify a new topic, it creates a decision tree based on the characteristic values of the available training data set.
Each node of the tree is generated by calculating the highest information gain for all attributes. If any attribute returns an undoubted result, the branch of that attribute is disabled and the target value is then assigned to it. The following diagram shows the sample decision tree.
A 12-fold cross-validation technique was used to build the model. It is as follows:
Divide the data into 12 sets of n / 12 sizes.
Train in 11 datasets and test on 1.
Repeat 12 times and take the average accuracy.
In the 12-fold cross-validation, the original sample was randomly divided into 12 equal-sized sub-samples. Then a single sub-sample is put into validation data to test the model and the remaining (12− 1) sub-models are used as training data.
Bayes Algorithm
It is based on the Bayes rule of conditional probability. It uses all the features in the data and analyzes them individually, even though they are equally important and independent of each other. The construction process for Naive Bayes is parallel.
This can be applied to a large dataset in real-time because it overcomes various limitations, such as ignoring complex iterations of the parameter. To create the model using this algorithm we used the 70:30 percent split technique. 70% of the data set was used to train the data and the other 30% was used to test the model.
SMO (Sequential Minimal Optimization)
This algorithm is commonly used to solve quadratic programming problems that arise during SVM (Support Vector Machines) training. SMO uses heuristics to divide the training problem into smaller problems that can be analytically solved. It replaces all missing values and converts the nominal attributes into binary. Also, all features are normalized by default, which helps speed up the training process. Here, too, this model
Dataset used:
Data were obtained from the Pima Indians Diabetes Database and the National Institute of Diabetes and Digestive and Kidney Diseases.
Procedure:
Load previous datasets to the system.
Data pre-processing was done by integrating the WEKA tool.
The following operations are performed in the dataset.
A. Replace the missing values.
B. Normalization of values.
The user inputs data to the system to determine if he has the disease.
Build three models using J48 Decision Tree, Nav Bayes, and SMO Support Vector Machine algorithms and train the data set.
Test the dataset using three models.
Get evaluation results.
Closing Point:
Considering these results, each model has more than 70% accuracy. Similarly, due to the voting process of all the algorithms, this ensures that the conclusion is very accurate.
Also, we planned to gather more data from different districts of the country and to increase more accurate and simple foresight patterns.
New drugs, procedures, and devices continually invented to improve the scope and quality of health services.Artificial intelligence, machine learning, data science, and other automation-based approaches have been found to increase human effort.Read more on Artificial Intelligence In Public HealthThey are easy to work with and bring impact and efficiency to work processes.
If we look at several significant developments, we can see that AI, machine learning, and data science are used for good reason.Motivation is understandable: New technologies allow services to be delivered more cheaply and to higher standards.Management of patient visitsOnline booking of AI and machine learning placements can help reduce the waiting line.
Patients, for example, are more likely to come to the hospital only when the time comes to see a doctor.These patients can also interact with AI-powered chatbots (Top 10 AI & ML Apps)that can meet their immediate health needs before access to a health professional.Digital management of patient information helps keep the hospital paperless and information retrieval very useful.
This allows physicians to focus on listening to patients’ concerns, and they can quickly enter data into electronic hospital records.There is also an increasing opportunity in telemedicine, where home care robots can connect patients with their doctors via video calls.
This eliminates the burden of having to check the patient from time to time.Diagnosis and PrescriptionMachines can use previous data to determine the presence of a disease condition.
Despite the more significant concern over the accuracy of the results, machine-based diagnosis and prescription are very accurate.
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Dr. Bill Releford is also known for many treatments related to neuropathy, diabetes, etc.
He is recognized domestically and internationally as a leader in the field of diabetes organ protection and wound care.
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This report aims to provide detailed insights into the global digital diabetes management market.
It provides valuable information on the type, procedure, application, and region in the market.
Download PDF Brochure: https://www.marketsandmarkets.com/pdfdownloadNew.asp?id=144725893 Key Factors Driving Market Growth: The major factors driving the growth of this market include the rising prevalence of diabetes, technological advancements, increasing adoption of cloud-based enterprise solutions, and the growing use of connected devices and apps.
By product & service, the devices segment accounted for the largest share of the Digital Diabetes Management Market Based on product & service, the market is divided into devices, apps, data management software & platforms, and services.
The growing demand for wireless and wearable devices for diabetes management, growing acceptance of smart insulin pumps & pens for insulin delivery, technological innovations & digital advancements in devices, and increasing awareness about continuous glucose monitoring in patients are the key factors driving the growth of the devices segment.
Wearable devices segment to witness the highest growth in the digital diabetes management devices market, by type Based on type, the digital diabetes management devices industry is segmented into wearable devices and handheld devices.
