Our work predicting the most relevant medicine or treatment for a patient on the basis of medical history, symptoms, and other associated data is a critical application of machine learning in healthcare. This will lead to modified medicine and more efficient treatment approaches. We work in a collaborative manner by identifying the best and the possible solution to create an apt topic idea as per scholar’s interest. Nearly five to eight Medicine Prediction topics will be suggested where scholars can choose any one and pursue further. Research Proposal will be clearly written in which the proposed framework and methodologies will be explained.
Here we had given guidance on constructing a medical forecasting system utilizing machine learning:
Define the primary aim: “To improve a machine learning method that forecast the most relevant medicine for a patient data on the basis of their medical history, symptoms, and other similar data”.
Predicting medicines by utilizing machine learning will transform modified medicine and aids in suggesting more efficient treatment plans. However, the utmost care will be taken to make sure the safety and efficiency in such serious applications.
Article writing under medicine prediction using machine learning will be well written by our expert writers as our team are full-fledged in resources and good in English vocabularies. Scholars can track our reports further modifications will be done and customised as per your request.
A complete track of updated tools and resources are available in phdservices.org so we guide you in latest thesis topics. Get our thesis writing services to excel in your academics. Our expert resource team are available 24/7 to help scholars solve their research issues. Some of our best and original topics that we have worked are as follows.
machine learning, explainable machine learning, Shapley values, acupuncture, cup, herb, alternative medicine
Our paper offers an outline to predict the efficiency of alternate medicine like acupuncture, cupping and the use of herbs. We can collect the medical record datasets of alternate medicine from the health center. Our XGBoost classifier model gives the framework outperformance with best accuracy rate. Shapely values can find the feature to classification decision with XGBoost classifier method.
Forecasting models; essential medicines; consumption data; health supply chain; Rwanda
Our paper focuses on the application of ML technique to predict the demand for necessary drugs in Rwanda. We can use the ML methods like Linear regression, Artificial Neural Network and Random Forest. Our random forest method test and train the dataset and can give the best performance as a forecasting model for the demand for medicine necessity. At last we used data-driven predictive model with ML could become the basis of health supply chain planning and operational management.
Generative Antagonistic Organization, Long Momentary Memory, Medication User, Multi-Facet Perceptron, Relapse, Time-Arrangement Expectation
We have been used ML methods to predict pharmaceutical industry based on the previous linked data and other health care features. Some of the ML methods utilized for healthcare are MLP, LSTM and CNN etc… LSTM has been worked as an initiator system to MLP model to build a Generative adversarial network (GAN). We used GAN based strategies for variance minimisation to analyse patient centric expense in health domain.
dental caries; random forest; logistic regression; gradient boot decision tree; support vector machine; artificial neural network; convolutional neural network; long short time memory
Artificial intelligence has been used now a day to improve the model that can predict the risk of dental caries. Our study gathers data from children’s oral care survey lead by Korean center for disease control and prevention. We used many ML methods to this data and their performance were calculated by metrics like accuracy, precision, F1score and recall. Our random forest method gives the best performance when compare to other ML methods.
COVID-19, Pneumonia, Chest X-ray, Medicine suggestion
Our study offers DL model of CNN solution for detecting covid-19 patients using X-ray images. We accessed publicly available datasets to train, test and validate the sets. Our method has a classification precision on test and validation set that is best for classifying covid-19 pneumonia and normal patients. We have to improve the application by utilizing flask framework and this can detect COVID positive and negative patients by using x-ray images and also decrease the number of false positive and false negatives in COVID-19 detection.
Data mining; Feature selection; Medicine prediction; Neural network
Our paper supposes to offer an evaluation of ebb and flow system of learning revelation of database by using information mining techniques therapeutic research particularly in medicine prediction. To select features we used correlation, chi-square and euclidean distance feature selection and this can compare the results among KNN, NB, DT and ANN.
Cancer Treatment, Genetic Mutation, Text Classification, Gene, Variation
Our paper uses different Machine Learning methods to predict cancer categories were maintained the data with genome sequence. To predict the correct variant for patient’s diagnosis will improve diagnosis rate. Genetic testing can interrupt diseases like cancer and any other disease. Our goal is to offer a ML based model by utilizing genome data to get guidance among personalized medication.
Forecasting; deep learning; prediction
Artificial intelligence application and predictive method have utilized ML and DL to construct prediction models. Our model allows optimization of inventory levels by decrease cost and potentially improve sales. Various metrics can be utilized in our paper to estimate the prediction method. Our goal is to ML and DL methods of prediction to get a best accuracy rate.
Acute myocardial infarction ; GEO ; Traditional Chinese medicine ; Bioinformatics
Our paper uses Gene Expression Omnibus (GEO) dataset join with ML was utilized to train differential genes in acute myocardial infarction (AMI). In our paper feature genes were screened by SVM and RF and the gained features were confirmed by the GSE61145 dataset. We also obtained six feature genes by SVM and RF like ZFP36, GADD45A, PELI1, METRNL, MMP9, and CXCL16.
Our paper built a combines DL prediction method of CEEMDAN-R-ILSTM-Elman. The prediction model is combined by complete ensemble empirical mode decomposition (CEEMDAN), improved long-term and short-term memory network (ILSTM), and Elman neural network. Our CEEMDAN-R-ILSTM-Elman integrated model predicts number of new case of COVID-19 in BRICS country with increased accuracy and lowering error.
Before sit down to research proposal writing, we need to decide exact journals. For e.g. SCI, SCI-E, ISI, SCOPUS.
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To ensure the novelty of research, we find research gaps in 50+ latest benchmark papers (IEEE, Springer, Elsevier, MDPI, Hindawi, etc.)
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Writing a good research proposal has need of lot of time. We only span a few to cover all major aspects (reference papers collection, deficiency finding, drawing system architecture, highlights novelty)
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For every project order, we deliver the following: reference papers, source codes screenshots, project video, installation and running procedures.
We intend to write a paper in customized layout. If you are interesting in any specific journal, we ready to support you. Otherwise we prepare in IEEE transaction level.
Before paper writing, we collect reliable resources such as 50+ journal papers, magazines, news, encyclopedia (books), benchmark datasets, and online resources.
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We play crucial role in this step since this is very important for scholar’s future. Our experts will help you in choosing high Impact Factor (SJR) journals for publishing.
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Paper published in online and we inform you with paper title, authors information, journal name volume, issue number, page number, and DOI link
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