Review on Pharmaceutical Industries Production of Medicines for Lung Cancer Diseases Prevention and Side Effects DataSets View and Analysis with Orange Software Visualization Techniques

Abstract

Purpose: The purpose of this research is to explore how Orange, a powerful information extraction and predictive modeling software, can be applied in the pharmaceutical industry to assess and visualize the effectiveness of cancer prevention medicines. By focusing on pharmaceutical companies like Genentech Inc. (USA), AstraZeneca Pharmaceutical PLC (UK), Boehringer Ingelheim (Germany), and Chugai Pharmaceutical Co. Ltd. (Japan), this study seeks to evaluate which cancer-preventing drugs from these companies provide the best efficacy while minimizing side effects for patients. The goal is to assist healthcare professionals (doctors and pharmacists) in making informed decisions about the most suitable medications for cancer prevention, ensuring patient safety and optimal treatment outcomes.
Design/Methodology/Approach: This research utilizes Orange software’s machine learning and data visualization tools, specifically scatterplot graphs, to analyze complex datasets related to cancer prevention drugs. By using scatterplots to concurrently examine multiple parameters, such as Company Name, Drug Class, Medicine Name, Prevention of Cancer Diseases, and Side Effects Percentage the study aims to identify patterns and correlations that can help pharmaceutical companies and healthcare professionals assess drug efficacy and safety. The approach involves analyzing the relationship between drug characteristics and side effects, providing actionable insights into how different treatments might interact with patient health conditions.
Findings/Results: The findings suggest that Orange’s scatterplot visualizations provide valuable insights into the effectiveness of various cancer prevention medicines across different pharmaceutical companies. By enabling the simultaneous analysis of multiple parameters, the software helps to identify which drugs are most effective in preventing cancer while minimizing side effects. This provides a clearer understanding of the correlations between drug characteristics, prevention outcomes, and side effects, supporting data-driven decision-making in pharmaceutical development and healthcare practices.
Originality/Value: The originality of this study lies in the application of Orange’s data mining and machine learning capabilities to visualize complex relationships within pharmaceutical datasets. The use of scatterplots to analyze drug efficacy, prevention outcomes, and side effects is an innovative approach that offers a richer, more nuanced understanding of cancer prevention drugs. This study contributes valuable insights into optimizing drug choice and treatment strategies, ultimately improving patient safety and therapeutic outcomes.
Paper Type: This is an analytical research paper that applies machine learning and data mining techniques to assess the effectiveness and safety of cancer prevention medicines. The research focuses on using Orange software’s visualization tools to extract and interpret complex data, providing actionable insights for pharmaceutical companies and healthcare professionals.