Medicine Prediction Using Machine Learning

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:

  1. Objective Definition

            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”.

  1. Data Collection:
  • Medical Databases: To get patient data that involves symptoms, diagnosis, and the suggested medicines.
  • Privacy: To make sure that our data is anonymized and fulfills with some protocols like GDBR or HIPAA.
  • Gather Related Data: We collect data that involves lab test results, previous medical treatments, genetic information (if available), lifestyle data etc.
  1. Data Preprocessing
  • Handling Missing Values: In our work some medical records have missing entries. We address this by imputation or deletion.
  • Feature Engineering: We develop new characters like the duration of illness, frequency of a specific indicator etc.
  • Encoding: Our work changes categorical variables (e.g., kinds of indicators or analysis) to a numerical format utilizing methods like one-hot encoding.
  • Feature Scaling: Regulate characters that have relevant magnitude.
  1. Model Selection and Development
  • Decision Trees & Random Forests: Our model offers a good initiating point due to their interpretability and facility to handle complex interactions.
  • Gradient Boosting Machines (e.g., XGBoost, LightGBM): This method develops new characters like the duration of illness, frequency of a certain indicator, etc.
  • Neural Networks: This method utilizes some enough data and the connections are complex.
  • Multi-label Classification Models: In this model if a patient is suggested multiple medicines, consider the models which hold multiple medicines, consider models that hold multiple results.
  1. Training the Model
  • We divide the data into three types training, validation and test sets.
  • Our work trains the model by utilizing the training set, and fine- tune utilizing the validation set.
  1. Model Evaluation
  • Metrics: Some of the metrics we utilized are accuracy, precision, recall and F1-score are standard. For multi-label challenges we consider the metrics like Hamming Loss or Jaccard Similarity.
  • Cost-sensitive evaluation: In medical application, certain forecasting mistakes have severe consequences. We allocate high costs to such mistakes and estimate consequently.
  1. Optimization & Hyperparameter Tuning
  • Our work regulates hyperparameters to increase performance. This involves the learning rate, depth of trees or to regularizing parameters.
  1. Deployment
  • Once satisfactory performance is attained, we assimilate the model into hospital systems or electronic health record (EHR) systems.
  • We offer a user-friendly interface for doctors or medical specialists to input patient data and acquire medicine recommendations.
  1. Feedback Loop
  • Doctors will be able to offer feedback on the models’ predictions. This feedback retrains the model and can be utilized for further improvement.
  1. Conclusions & Future Enhancements
  • Our work outlines the projects performance and tasks.
  • In future enhancements we include join more data sources, real-time forecasting as patient data is updated or forecasting possible side-effects of medicines.


  • Expert Collaboration: We work together with medical specialists to understand the data better and estimate the model’s forecasting.
  • Ethical and Safety Considerations: To make sure always that the machine learning method helps as a recommendation tool and that do not change the proficiency of a doctor. To make sure that the forecasting are protected and ethically sound.

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.

Medicine Prediction using Machine Learning Projects

Medicine Prediction Using Machine Learning Thesis Topics

A complete track of updated tools and resources are available in 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.

  1. Explainable Prediction of Alternative Medicine Outcome using Machine Learning and Shapley Values


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.  

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


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.

  1. Medicine Expenditure Prediction using Machine Learning.


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.

  1. DCP: Prediction of Dental Caries Using Machine Learning in Personalized Medicine


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. 

  1. Diagnosis and Medicine Prediction for COVID-19 Using Machine Learning Approach


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.

  1. Medicine prediction based on doctor’s degree: a data mining approach


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.

  1. Multiclass classification models for Personalized Medicine prediction based on patients Genetic Variants


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.   

  1. Predicting medicine demand using deep learning techniques: A review


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.

  1. Integrating bioinformatics to identify and analyze feature genes of acute myocardial infarction and potential Chinese medicine prediction


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.

  1. Computational and Mathematical Methods in Medicine Prediction of COVID-19 in BRICS Countries: An Integrated Deep Learning Model of CEEMDAN-R-ILSTM-Elman


            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.


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