Role of Machine Learning in Healthcare Sector

Rastogi, Mitanshi; Vijarania, Meenu; Goel, Neha

doi:10.2139/ssrn.4195384

Cited by 8 publications

(3 citation statements)

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“…In order to get better at predicting new data, models evaluate themselves during training using these labelled results [16]. Decision Trees (DT), Support Vector Machines (SVM), Random Forests (RF), logistic and linear regressions, neural networks, Naïve Bayes Models could be trained via supervised learning [17], claims Kotsiantis. The two primary categories of supervised learning algorithms are regression and classification.…”

Section: A Supervised Learning Algorithmmentioning

confidence: 99%

“…Machine Learning is a subfield of Artificial Intelligence and it is referred to as "the study of computer systems with the capacity to learn from information and experience". It is an area of research that allows computers to learn without explicit programming [1]. The medical field now has many opportunities to effectively address and resolve health care issues thanks to machine learning technology.…”

Section: Introductionmentioning

confidence: 99%

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The Significance of Machine Learning in Healthcare Data Management

Aligbe

2023

Preprint

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Healthcare data management in machine Learning is the application of different machine learning techniques to analyse and interpret large amounts of healthcare data, including patient records, administrative data, clinical data, and much more. The history of machine learning, as well as some fundamental knowledge of these methods and the various roles they have played in healthcare data management, are briefly explored in this paper. The impact of machine learning on data collection, analysis, predictive modelling, decision-making procedures, and overall healthcare improvement is given special consideration. The healthcare sector has greatly benefited from machine learning since it can process a variety of datasets and provide early disease diagnosis, individualised treatment plans, disease prevention, and anomaly detection. To predict this disease, various supervised machine learning algorithms are used.

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Section: A Supervised Learning Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Significance of Machine Learning in Healthcare Data Management

Aligbe

2023

Preprint

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“…The diagnosis of cancer is made by doctors who are experts in the field by interpreting the observable symptoms and the results of examinations. Intensive studies in the field of artificial intelligence [15][16][17], together with rapidly developing technology, have paved the way for machines to make the right decisions in many areas, just like human experts. The branch of artificial intelligence that enables predictions about new situations by using correlations obtained from data by interpreting the data of the current situation is called machine learning (ML) [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Machine learning-based lung cancer diagnosis

Dirik

2023

Turkish Journal of Engineering

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Cancer is one of the leading health problems occurring in various organs and tissues of the body and its incidence is increasing in the world. Lung cancer is one of the deadliest types of cancer. Due to its worldwide prevalence, increasing number of cases and deadly consequences, early detection of lung cancer, as with all other cancers, greatly increases the chances of survival. As with all other diseases, the diagnosis of cancer becomes possible after the appearance of various symptoms through the examinations of specialists. The recognizable symptoms of lung cancer include shortness of breath, coughing, wheezing, jaundice in the fingers, chest pain and difficulty swallowing. The diagnosis is made by an expert on site based on these symptoms and additional tests. The aim of this study is to detect the disease at an earlier stage based on the symptoms present, to assess more cases with less time and cost, and to achieve results in new situations that are as successful or even faster than those of human experts by deriving them from existing data using various algorithms. The goal is to develop an automated model that can detect early-stage lung cancer based on machine learning methods. The developed model includes 9 different machine learning algorithms (NB, LR, DT, RF, GB, SVM). The success of the classification algorithms used was evaluated using the metrics of accuracy, sensitivity and precision calculated with the parameters of the confusion matrix. The results obtained show that the proposed model can detect cancer diagnosis with a maximum accuracy of 91%. The application of this model will help medical practitioners to develop an automated and reliable system that can detect lung cancer. The proposed interdisciplinary method can also be applied to other types of cancer.

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