Data Mining Healthcare Research Topics

Data Mining health care research topics is one of the most important techniques to handle the large datasets and to extract the valuable insights. In this work we utilize data mining techniques to overcome the existing issues and to obtain possible outcomes. Below we provide several innovative ways of utilizing data mining techniques and present some research topics and thoughts in data mining:

1. Define Data Mining?

Initially we look the definition of Data Mining; it is the process that creates significant specimens, trends and perceptions that were taken from the big volume of data by incorporating different methods like statistical analysis machine learning and artificial intelligence.

2. What is Data Mining?

Next to the definition of Data Mining we go through the in-depth explanations of Data Mining. It is the method of extracting important understandings and patterns on large datasets, incorporating the methods like statistical, mathematical and machine learning to find the invisible connection and trends.

3. Where Data Mining used?

After looking the Data Mining explanations, we look through where it will be used. It is incorporated in different domains such as examination of the market and service for client administration, for forecasting modeling and diagnosis for healthcare, fraud identification in finance and from knowledge creation from experimental data for scientific research.

4. Why Data Mining technology proposed? , previous technology issues

Data Mining can handle large volume of data and it is the extraction of significant perceptions. So we propose this Data Mining in our work to overcome the existing issues. We employ this method because it manages the large and complicated datasets effectively by extracting the important understandings for decision-making. Some of the difficulties that face the existing methods are processing large quantity of data and struggle on innovative analytical capabilities, creating Data Mining a technological creativeness to tackle these limitations.

5. Algorithms / protocols

We propose a Data Mining technique to overcome the difficulties in existing works, here we discuss some algorithms to be utilized for Data Mining techniques are Generative Adversarial Network, Homomorphic encryption, Laplacian mechanism and Leach protocol are employed in our work.

6. Comparative study / Analysis

Succeeding the algorithms that we utilized in this work, we converse about the comparative analysis that compares the various existing methods to get the corresponding outcomes. Following are some of the methods that we compared are:

• Cleveland Heart Disease dataset is the dataset we utilized to control healthcare. We make sure that the classes are balanced by employing the Synthetic Minority Oversampling Technique and in addition we extract the features so as to minimize the dimensionality of the data by employing the Principal Component analysis (PCA).
• GAN with ADAM optimization is used by us in order to perform classification with the distinction made across normal, high and medium health classifications.
• We make sure that all the interactions are maintained confidentially and safely by employing the Laplacian technique with homomorphic encryption.
• The Leech method is executed to verify the effective data routing, and the data is preserved in an environment that uses edge cloud computing.
• By continuous record analysis we detect healthcare at the day end, which activate alarm alerts to give assurance that timely replies are taken to possible health issues.

7. Simulation results / Parameters

The comparative analysis compares various existing methods to give better solution and then afterwards we compare the parameters to obtain the corresponding results. The parameters that we compared are accuracy (%) and precision (%) with the Number of epochs and authentication time (s), throughput (%) and packet delivery ratio (%) with the Number of users. These are the parameters that we compared to get the corresponding results.

8. Dataset LINKS / Important URL

In this research after completing comparison among different parameters, we provide some important links to describe the Data Mining applications, uses, explanations etc. to clarify the doubts and some additional references we go through this links to clear them:

• https://www.mdpi.com/2079-9292/11/15/2292/pdf
• https://link.springer.com/chapter/10.1007/978-3-030-84182-9_3
• https://www.mdpi.com/2071-1050/15/3/1862/pdf
• https://www.sciencedirect.com/science/article/pii/S016926072300411X

9. Data Mining Applications

Here we propose the data mining technology that is broadly utilized in many applications namely customer relationship management to forecast and examine customer behavior, identification of fraud in finance with unusual pattern finding and healthcare for predictive modeling in disease diagnosis and planning the treatment.

10. Topology for Data Mining

Now we discuss the topology for Data Mining, in different topologies the Data Mining methods are implemented, such as Decision Trees, Neural Networks and Clustering structures, on the basis of particular analysis and objectives.

11. Environment for Data Mining

Following the topology for Data Mining techniques, we carried out Data Mining in different environments that range from standalone desktop applications to distributed computing environments. The cloud based environments generally raises the scalability and availability, enabling efficient analysis of large data sets.

12. Simulation tools

The proposed method follows the succeeding software requirements such as the dataset here we utilized are Cleveland Heart Disease dataset and the tool that employed is python 3.11.4 and it is implemented on the operating system namely Windows 10 [64 bit]. The above mentioned are the simulation tools that we incorporated in this work.

13. Results

Data mining is one of the current crucial techniques to be utilized in research. We also propose this technique to overcome the existing technology issues and utilize these Data Mining technique to obtain the successful outcomes and these outcomes were compared with various parameters and this can be implemented on windows 10 [64 bit] operating system.

Data Mining Research Ideas:

Below we provide are some of the research topics on the basis of Data Mining techniques, these topics are useful when we look for the descriptions of Data Mining:

1. Data Mining in Healthcare using Machine Learning Techniques
2. The Development of Machine Learning innovation technology for Data Mining In Smart Healthcare
3. Research on Medical Big Data Mining and Intelligent Analysis for Smart Healthcare
4. Modeling of Metaheuristics with Deep Transfer Learning Based Visual Data Mining for the Healthcare Sector
5. Probabilistic Neural Network Based Visual Data Mining for Healthcare Sector
6. Data Mining Approaches in Healthcare Industry
7. Information Fusion for Seveirty Detection using Machine Learning in Data Mining Healthcare
8. A Data Mining Algorithm for Optimization of the Path of Financial Management
9. Privacy Preserving Data Mining and its Applications: A Survey of Recent Developments
10. Data-Mining-Based Hardware-Efficient Neural Network Controller for DC–DC Switching Converters
11. Simulation of Computer Network Information Security Assessment Model Based on Data Mining
12. A Comparison of Data Mining Approaches for Forecasting Sales of FMCG Food Products
13. A Comprehensive review on Data Mining Techniques in managing the Medical Data cloud and its security constraints with the maintained of the communication networks
14. A Flexible Monotone Neural Network for Data Mining
15. Sports Score Management and Physical Fitness Analysis System Based on Data Mining
16. Internet Financial Data Mining and Analysis Based on Internet Data
17. Design and Implementation of Digital Book Recommendation Platform Based on Data Mining Visualization Technology
18. Reconstruction Optimization of Economic Operation of Distribution Network Based on Data Mining Algorithm
19. Developing and Evaluating Data-Driven Heart Disease Prediction Models by Ensemble Methods on Different Data Mining Tools
20. Data Mining and Visualization to Understand Employee Attrition and Work Performance
21. WePaMaDM-Outlier Detection: Weighted Outlier Detection using Pattern Approaches for Mass Data Mining
22. Asleep Adults’ Breathing Patterns via Data Mining of Electromyograms
23. Spatial Data Mining in Aerial Object Detection Datasets for Finding Co-Locations and Anomalies
24. Real time operation data mining algorithm for power systems based on adaptive incremental clustering algorithm
25. Design and Application of Regional Economic Data Analysis System Based on Data Mining Model
26. Automating Decision-Making for Hiring Brilliant People While Taking Risk Factors Into Account: A Data Mining Approach
27. Consumption Level Classification System Based on Clustering Algorithm and Data Mining
28. Business Research and Data Mining: a Bibliometric Analysis
29. Application Of Data Mining Technologies In The Management Of Educational Content On Social Networks In E-Learning
30. Consumer review Analysis using NLP and Data Mining
31. Implementation of data mining algorithm based on Cluster analysis for sports training technique and tactics analysis
32. Role of Educational Data Mining and Learning Analytics Techniques Used for Predictive Modeling
33. Research on the Influence of Blended Writing Teaching Model Based on data mining on College Students’ English Writing Ability
34. Research on scientific data mining algorithms based on WOA-BP neural networks
35. Data Mining Model for Forecasting Academic Performance of Undergraduates Based on Behavioral Features and SVM in E-learning
36. Design and Realization of Efficiency Evaluation of Enterprise Operation Based on Data Mining
37. What is My Problem? Identifying Formal Tasks and Metrics in Data Mining on the Basis of Measurement Theory
38. Evaluation on the Application of Data Mining in the Evaluation System of Teaching Ability of Double Teacher Teachers
39. Comprehensive Budget Management of Power Grid Enterprises Based on Data Mining
40. Modeling of Metaheuristics with Deep Transfer Learning Based Visual Data Mining for the Healthcare Sector
41. Application of Data Mining in Depth Analysis of Drug Use
42. The Use of Data Mining Techniques to Predict Employee Performance: A Literature Review
43. Simulation Research on Network News Information Dissemination Model Based on Data Mining Algorithm
44. Application of Fuzzy Data Mining Algorithm in Human Resource Management
45. Analysis of Website in Web Data Mining using Web Log Expert Tool
46. A University Innovation and Entrepreneurship Information Sharing Platform Based on Data Mining and Classification Algorithms
47. The Development of Machine Learning innovation technology for Data Mining In Smart Healthcare
48. Methods of Data Mining and Their Industrial Applications
49. Construction of New Energy Vehicle User Segmentation Model Based on Data Mining Algorithm
50. Application of Data Mining Algorithm in Computer Network Optimization Design