The deep DNA machine learning model to classify the tumor genome of patients with tumor sequencing

J Logeshwaran; Nirmal Adhikari; Sidharth Srikant Joshi; Poorvi Saxena; Ankita Sharma

doi:10.53730/ijhs.v6nS5.10767

Authors

Logeshwaran J
eshwaranece91@gmail.com
Research Scholar, Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, Coimbatore
Nirmal Adhikari Leeds Beckett University
Sidharth Srikant Joshi MDS Periodontology, Assistant Professor, Department of Periodontology, Aditya Dental College and Hospital, Beed Maharashtra
Poorvi Saxena Senior Lecturer, Department Of Conservative Dentistry and Endodontics, Geetanjali Dental and research institute, Udaipur
Ankita Sharma MDS, Department of Pediatric and Preventive Dentistry, Amritsar

Keywords:

medical systems, tumor genome, tumor sequencing, DNA, genetic testing, clinical decision-making, machine learning

Abstract

In general, the various medical systems currently available provide insights into changes in the tumor genome of patients with tumor sequencing. Most of the tumor DNA sequencing can also be referred to as genetic specification or genetic testing. The sequence results help clinical decision-making to develop a personalized cancer treatment plan based on the molecular characteristics of the tumor rather than a one-size-fits-all treatment approach. The tumor sequencing also plays a major role in cancer research. In this paper, an improved method based on machine learning was proposed to analyze the sequencing and tumor sequencing patterns of the human gene. This proposed method analyzes the circulatory problems of patients with different tumor types for analysis in the public domain. It also constantly monitors large data sets of cancer or tumor genetic sequences to calculate tumor size and location. This allows the doctor to get an accurate report on the type of tumor and the problems it can cause to the patient. The Analysis of these datasets of cancer tumor gene sequences reveals that the genetic makeup of each patient is different and that no two cancers are the same.

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