Healthcare Data Analytics Using Machine Learning Approaches

Registration ID: IJNRD_309889

Published ID: IJNRD2510144

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N.Y.V.V.S.S.THANUJA, S.MANO VENKAT

Machine learning, cardio vascular, healthcare, data mining, diagnosis

Data has become an integral part of the digital world with the advancement in computing technologies. The collection of data is very crucial with regards to data analytics. Every industry makes use of data analytics ranging from financial to other commercial applications but it becomes even more important in healthcare domain for the analysis of healthcare data. The present research work is mainly focused on classification/prediction problems of healthcare data based on machine learning (supervised) approaches using data mining techniques. There is a need to design an intelligent model (based on machine learning) which can classify the amount of data that is stored in our databases. Human data analytical capability rate is much smaller when compared to the amount of data that is stored. This (classification) becomes even more critical when it comes to healthcare data as it can help to detect, diagnose and treat the patients based on these classified data. These healthcare data are voluminous, high dimensional and diversified in nature which in turn demands more and more data mining applications for classification/prediction purposes. Machine learning has emerged as a significant tool in healthcare research to solve complex classification problems efficiently, effectively and quickly. In general, treatment of diseases is done by physicians based on their clinical knowledge and personal experience. Since physician’s experience varies from expert to expert, they may sometimes diagnose the cases inaccurately and may take more time to do manual treatment. In addition, demand of medical experts is also increasing every-day with population growth. Keeping in mind all these, computer-based Medical Disease Diagnosis Systems (MDDSs) are developed (on the basis of healthcare data) that can empower clinicians to make a timely and better-informed healthcare decisions. However, there are various issues and practical restrictions associated with health-related information among which the most important being, certain class of diseases may occur relatively in small number of patients, which in turn arises a data unbalancing problem in the medical datasets. Besides, certain diseases have low prevalence, so obtaining large volume of patients representing those diseases can be consistently troublesome. Third, the clinical appearance of patients with the same medical condition differs substantially and it causes conflicting issues. As a result of this uncertainty, MDDSs are often expected to deal with a wide range of features. The main goal of the thesis is to develop a machine learning-based model for classification tasks using several standard machine learning algorithms like decision tree, logistic regression, support vector machine, neural network, naïve bayes etc. in order to address the issues mentioned above. There are several Medical Disease Diagnosis System (MDDSs) but unfortunately most of them suffer from one or more drawbacks such as disease-specific clinical model, black box diagnostic model, statistical approaches based clinical model etc. The major contribution of the presented study is to address the issues mentioned above and are briefly discussed below- 1. A case study on healthcare bigdata is conducted to show the way how the size of healthcare data is rapidly increasing, importance of healthcare big data and its challenges, the latest trends to manage and process such data to provide quality health services. 2. Healthcare data sets are usually complex in nature that causes degradation of overall performance of the developed diagnosis system. The thesis includes an innovative entropy-based work to improve the performance of the system, removing the irrelevant features from the medical datasets. 3. Attention is paid to draw awareness of the researchers on the neglected cases like Preterm Birth (PTB) which is a serious public health problem that adversely affects both families and the society. A machine learning based model is introduced to predict PTB cases in the earlier stages itself. For this purpose, three learner classifiers namely, Logistic Regression, Support Vector Machine (SVM) and Decision Tree (DT) are used along with Minimum Information Loss (MIL) discretizer. 4. Specific work related to cardiovaslar disease is introduced to identify the effect of behavioural data on each of the risk factors and predict the occurrence of cardiovascular diseases. A neural network (NN)-based prediction model is designed for cardiovascular diseases. This model is evaluated against four different models namely, Linear Regression (LR), Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM). 5. The performances of the models are evaluated in terms of specificity, sensitivity and accuracy. All the experiments are carried out under WEKA tools and TensorFlow framework. The results obtained during the experiments suggest that the performance of the introduced learning models is better than its competent models. In fact, the introduced MDDS can be used in healthcare domain to improve the diagnostic speed, accuracy and reliability. Future studies can be explored by developing MDDS for healthcare big data using big data analytical tools and techniques. Security concerns should also be taken as future opportunities. Keywords: Healthcare, Data Analytics, MDDS, Machine Learning, Classification, Feature Selection, PTB, Learning Classifiers, Data Mining.

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N.Y.V.V.S.S.THANUJA & S.MANO VENKAT (October-2025). Healthcare Data Analytics Using Machine Learning Approaches . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 10(10), b342-b354. https://ijnrd.org/papers/IJNRD2510144.pdf

Volume 10 Issue 10, October-2025

Pages : b342-b354

Paper Reg. ID: IJNRD_309889

Published Paper Id: IJNRD2510144

Research Area: Science and Technology

Author Type: Indian Author

Country: VISAKHAPATNAM, ANDHRA PRADESH, India

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