A Review of Managed Learning Process for Constructing Decision Trees in Clinical Diagnosis
Abstract
In this paper, we introduce a managed learning approach for constructing a decision tree aimed at clinical diagnosis. Our primary goal is to develop an efficient classification model with high recall and moderate precision to enhance the efficiency and effectiveness of disease prediction processes. We employed the ID3 algorithm for decision tree construction, and the final model was assessed using standard evaluation methods. This model offers a systematic framework for leveraging relevant information in clinical data, particularly aspects often overlooked by existing methods overly focused on high predictive accuracy. Our analysis was conducted on datasets related to diabetes and coronary disease sourced from the UCI repository. Test results highlight the decision tree's significant contribution to classification quality. Based on these findings, we conclude that decision trees are particularly suitable for addressing disease prediction classification challenges and advocate for their adoption in similar classification tasks.
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Introduction
With the rapid advancement of both data technology and networking, a plethora of transactions generate vast amounts of data daily. While raw data alone may not yield direct benefits, effective mining is necessary to extract hidden insights from this abundance of information. Data mining involves the search for interesting patterns or knowledge within large datasets, transforming raw data into actionable insights. It serves as a crucial step in the data discovery process, enabling analysis from diverse perspectives and conversion into valuable information. Widely applied across various domains such as medical diagnosis, intrusion detection systems, education, banking, and fraud detection, data mining encompasses supervised learning techniques like classification, prediction, and grouping. Classification, in particular, involves a two-step process: learning, where training datasets are analyzed by classification algorithms to derive classification rules or patterns, and application, where the learned model is utilized to classify new data and evaluate accuracy. Decision trees play a pivotal role in data mining and analysis, utilizing a set of training data to generate a tree structure that accurately classifies the data. The decision tree methodology has gained popularity in clinical research, notably in disease diagnosis scenarios where it aids in identifying ailments based on symptom patterns, potentially guiding treatment decisions.[4,6]
Conclusion
The clinical dataset in the different information mining and the AI strategies are accessible and afterward the significant part of clinical information mining is to expand the exactness and effectiveness of sickness finding. The goal of this examination work is planned to show the classes of clinical information from the accessible crude clinical dataset assists the doctor with showing up at a precise finding. The outcomes are assessed dependent on the precision of arrangement is 94% for diabetes information and 82% for coronary illness information. Subsequently decision tree classifier is proposed for analysis of clinical determination expectation-based order to improve results with precision and execution.