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Total Size:
7.2 MB
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16BDBDD89BBCA40608E3024380946FAC22B64CF2
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Sept. 22, 2025, 8:54 a.m.
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(Last updated: Sept. 22, 2025, 8:57 a.m.)
| File | Size |
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| Malviya R. Machine Learning in Healthcare. Advances and Future Prospects 2026.pdf | 7.2 MB |
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27.1 MB
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2024-11-02
| Uploaded by indexFroggy | Size 27.1 MB | Health [ 17 /3 ] | Added 2024-11-02 |
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14.3 MB
[26
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2024-12-31
| Uploaded by indexFroggy | Size 14.3 MB | Health [ 26 /3 ] | Added 2024-12-31 |
NOTE
SOURCE: Malviya R. Machine Learning in Healthcare. Advances and Future Prospects 2026
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COVER
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MEDIAINFO
Textbook in PDF format This new volume explores the integration of Machine Learning in healthcare, which has transformed technology for disease diagnosis, treatment, and management. The book shows the enormous possibilities made possible by computational technologies, ranging from analyzing electronic health information to predicting, detecting, and treating cancer, cardiovascular disease, thyroid disorders, and diabetes. The exploration extends beyond conventional domains, discussing topics such as wearable devices and mental health management through the use of Machine Learning technology. The term “machine learning,” a subfield of Artificial Intelligence (AI), refers to a set of cutting-edge computational technologies that have found applications in a diverse range of fields, including the healthcare sector. There is a growing trend among individuals to utilize Machine Learning tools for disease diagnosis and forecasting associated healthcare costs. Numerous empirical investigations and real-world applications have demonstrated that Machine Learning-based disease diagnostics (MLBDD) exhibit significant promise as an economically viable and efficient approach for the identification of diseases. The commonly employed conventional diagnostic techniques are characterized by their demanding nature in terms of labor, time consumption, and cost. Robotic systems possess the advantage of perpetual operation without experiencing fatigue, in contrast to Machine Learning-based systems that are not constrained by the user’s proficiency level, as well as human doctors. Machine Learning, being a multidimensional subject that encompasses various disciplines such as statistics, mathematics, data management, and knowledge analytics, presents a formidable task in providing a single term. Machine Learning refers to the branch of AI that enables systems to acquire knowledge and improve performance by analyzing and interpreting real-world data. Numerous data mining algorithms have been developed because of extensive research in the field of data mining. These strategies can be directly applied to a dataset to construct models or extract valuable insights. In the area of data mining, commonly employed techniques include decision trees (DTs), naive bayes (NB), k-means, artificial neural networks (ANNs), and similar methodologies. The random forest (RF) algorithm is a popular technique in Machine Learning that utilizes ensemble learning to perform both classification and regression tasks. In the training phase, several separate DTs are formed, with each tree generating a singular prediction regarding the target class. The final output is the most frequently taken class in the field of forecasting. The objective is to find a point of agreement by employing the method of averaging to mitigate the effects of both significant disparities and pronounced bias. Both R and Python offer libraries that provide robust support for this methodology. Preface Machine Learning Algorithms in Disease Diagnosis and Management Machine Learning-Based Diagnosis and Treatment of Cancer Machine Learning-Based Detection and Management of Cardiovascular Diseases Monitoring the Health Status of Thyroid Patients Using Machine Learning Machine Learning-Based Wearable Devices for Healthcare Applications Prediction of Diabetes Using Machine Learning Mental Health Index Management Using Machine Learning Machine Learning Approaches for Electronic Health Record Phenotyping
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