Introduction:

 

Data mining, also referred to as data "knowledge mining," plays a crucial role in extracting valuable insights and patterns from large datasets. In the medical field, data mining has proven to be an essential tool for predicting and diagnosing various diseases, including breast cancer and heart disease. The goal of this study is to analyze different data mining techniques to improve the prediction accuracy of medical conditions and enhance healthcare services.


Objective:

 

This research aims to explore various data mining tools and methodologies to predict medical conditions with greater accuracy and efficiency. Additionally, the study intends to address challenges related to data mining in medical practice and discuss the implications of drug abuse detection using data mining techniques.

 

Methodology:

 

To achieve the objectives of this study, a comparative analysis will be conducted on various data mining techniques employed in healthcare. The research will involve collecting data from previous studies, scientific journals, and conference papers published in the last five to six years. The gathered data will be classified based on the data mining methods used in each study.

 

Literature Review:

 

Numerous studies have explored the application of data mining in healthcare, particularly in predicting heart diseases, cancer, diabetes, and HIV. Different data mining techniques such as association rule mining, classification, clustering, and forecasting have been utilized to achieve higher precision in disease prediction. Video mining has also emerged as an important area of research, focusing on extracting relevant information from video data, including audio, video, and text. Additionally, data mining techniques have been employed to facilitate medical image retrieval and support decision-making in healthcare systems.

 

Results:

 

The results of previous studies have shown promising outcomes in predicting breast cancer survivability using data mining methods like decision trees, neural networks, and logistic regression algorithms. Data mining techniques, such as support vector machine and decision trees, have demonstrated high accuracy in predicting heart disease, making them valuable tools in healthcare.

 

Conclusion:

 

Data mining has emerged as a critical technology for extracting valuable insights from vast amounts of medical data. By employing various data mining techniques, healthcare providers can improve diagnosis accuracy, optimize treatment plans, and enhance patient outcomes. Embracing data mining technology in the medical field holds immense potential for revolutionizing healthcare services and patient care.