Predictive Analytics: A Review of Trends and Techniques

Kumar, Vaibhav; Garg, M. L.

doi:10.5120/ijca2018917434

Cited by 86 publications

(61 citation statements)

References 16 publications

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“…Predictive analysis is a technique that forecasts unknown variables by answering the question "what might happen?" based on the study of collected data (Kumar & Garg, 2018). Several techniques, such as mathematical statistics and modeling, may be utilized in this case.…”

Section: Analysis Of Quantitative Datamentioning

confidence: 99%

Explanatory sequential design of mixed methods research: Phases and challenges

Toyon

2021

IJRBS

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The purpose of this essay is to discuss the phases and challenges of the explanatory sequential design (ESD hereinafter) of mixed methods research (MMR hereinafter) by reviewing relevant literature. The literature was explored during the design stage of a Ph.D. project that sought to examine the relationship among social capital, education, and employment for foreign students graduating from several Estonian universities. The review finds that the explanatory sequential design of MMR is much more complex than just sequencing how and what kind of data to collect; it also entails selecting how data will be processed and presented using a range of techniques that are often riddled with difficulties. By addressing these ideas, this paper will aid those interested in comprehending the summary of the explanatory sequential design of MMR.

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Section: Analysis Of Quantitative Datamentioning

confidence: 99%

Explanatory sequential design of mixed methods research: Phases and challenges

Toyon

2021

IJRBS

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“…Predictive modeling is an approach that use a mathematical method to foresee future occurrences or outcomes, as well as to predict future trends, by searching for patterns that have occurred in the past or by analyzing historical data [20]. In this point of regards, businesses with limited resources, based on experience, can use readily available software to develop reliable and accurate estimates in a timely and cost-effective manner using excel spreadsheets or other relatively simple excel-based software [19].…”

Section: Prediction Of Essential Medicines Demand With Machine Learning Techniquesmentioning

confidence: 99%

“…In this point of regards, businesses with limited resources, based on experience, can use readily available software to develop reliable and accurate estimates in a timely and cost-effective manner using excel spreadsheets or other relatively simple excel-based software [19]. Even though these are simple procedures with limited insight, it was evidenced that they are indeed widely used with an estimated rate of 82.1% [20]. Indeed, in the field of health supply chain, the purpose of predictive modeling is to answer to the concern regarding the use of known past behavior, for anticipating the most likely scenario to happen in the future [21].…”

Section: Prediction Of Essential Medicines Demand With Machine Learning Techniquesmentioning

confidence: 99%

The Prediction of Essential Medicines Demand: A Machine Learning Approach Using Consumption Data in Rwanda

et al. 2021

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Today’s global business trends are causing a significant and complex data revolution in the healthcare industry, culminating in the use of artificial intelligence and predictive modeling to improve health outcomes and performance. The dataset, which was referred to is based on consumption data from 2015 to 2019, included approximately 500 goods. Based on a series of data pre-processing activities, the top ten (10) essential medicines most used were chosen, namely cotrimoxazole 480 mg, amoxicillin 250 mg, paracetamol 500 mg, oral rehydration salts (O.R.S) sachet 20.5 g, chlorpheniramine 4 mg, nevirapine 200 mg, aminophylline 100 mg, artemether 20 mg + lumefantrine (AL) 120 mg, Cromoglycate ophthalmic. Our study concentrated on the application of machine learning (ML) to forecast future trends in the demand for essential drugs in Rwanda. The following models were created and applied: linear regression, artificial neural network, and random forest. The random forest was able to predict 10 selected medicines with an accuracy of 88 percent with the train set and 76 percent with the test set, and it can thus be used to forecast future demand based on past consumption data by inputting a month, year, district, and medicine name. According to our findings, the random Forest model performed well as a forecasting model for the demand for essential medicines. Finally, data-driven predictive modeling with machine learning (ML) could become the cornerstone of health supply chain planning and operational management.

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“…In Figure 7, we present a classification outline with SVM for two classes. If we use more than two classes, then two approaches, one-against-one and one-against-all, were used in the experiment are defined in [30,31].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Monitoring Movements of Ataxia Patient by Using UWB Technology

Zilani

Al‐Turjman²,

Khan

et al. 2020

Sensors

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Internet of multimedia things (IoMT) driving innovative product development in health care applications. IoMT requires delay-sensitive and higher bandwidth devices. Ultra-wideband (UWB) technology is a promising solution to improve communication between devices, tracking and monitoring of patients. In the future, this technology has the capability to expand the IoMT world with new capabilities and more devices can be integrated. At the present time, some people face different types of physiological problems because of the damage in different areas of the central nervous system. Thus, they lose their balance coordination. One of these types of coordination problems is named Ataxia, in which patients are unable to control their body movements. This kind of coordination disorder needs a proper supervision system for the caretaker. Previous Ataxia assessment methods are cumbersome and cannot handle regular monitoring and tracking of patients. One of the most challenging tasks is to detect different walking abnormalities of Ataxia patients. In our paper, we present a technique for monitoring and tracking of a patient with the help of UWB technology. This method expands the real-time location systems (RTLS) in the indoor environment by placing wearable receiving tags on the body of Ataxia patients. The location and four different walking movement data are collected by UWB transceiver for the classification and prediction in the two-dimensional path. For accurate classification, we use a support vector machine (SVM) algorithm to clarify the movement variations. Our proposed examined result successfully achieved and the accuracy is above 95%.

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Predictive Analytics: A Review of Trends and Techniques

Cited by 86 publications

References 16 publications

Explanatory sequential design of mixed methods research: Phases and challenges

Explanatory sequential design of mixed methods research: Phases and challenges

The Prediction of Essential Medicines Demand: A Machine Learning Approach Using Consumption Data in Rwanda

Monitoring Movements of Ataxia Patient by Using UWB Technology

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