Drug Recommendation System Using Machine Learning

By using machine learning, we develop a drug recommendation system that is determined attempt to approach with care because of possible life-and-death implications. The initial process is recommending the appropriate drugs depend on patient’s medical history, genetic makeup or symptoms. Remember this, every time seek advice from healthcare experts before making any decisions.

Desk based research work are carried by us to frame research topics we assure that no one has attempted it previously. Only by machine learning experts’ topics will be recommended. We also attach along all reference papers so as to support our reach work.

The following points help us to create the drug recommendation project,

  1. Objective Definition

The drugs are suggested appropriately for a given patient profile, medical condition and symptoms.

  1. Data Collection
  • Patient Data: This data holds age, gender, medical history, current medications, allergies and genetic information etc.
  • Drug Data: Drug name, dosage, side effects, indications, interactions and contraindications are involved in the drug data.
  • Outcome Data: It depicts us the result of patient after consuming particular drugs such as recovery rates, adverse effects etc.
  1. Data Preprocessing

The missing values are controlled properly. Using encoding techniques, we convert the categorical data into numerical format eg) one-hot encoding. The numerical data represented in an ordered manner.

  1. Feature Engineering

Integrate them with the related features. For example, total number of current medications. The new features are extracted by us similar to the presence of specific genetic markers affecting drug metabolism.

  1. Model Selection and Training
  • Collaborative Filtering: It suggests drugs based on parallel patients’ data.
  • Content-Based Filtering: The drugs are recommended by us depends on the patient’s profile and features of drugs.
  • Hybrid Approaches: We bind the collaborative and content-based techniques for the improved performance.
  • Deep Learning: This learning utilizes neural networks particularly for complex datasets or to catch the non-linear relationships.
  1. Evaluation
  • Accuracy: The model must recommend the accurate drug.
  • Precision: Make sure of the benefits of suggested drugs.
  • Recall: It ensures that all capable beneficial drugs are determined for our project.
  • F1-Score: We maintain a balance between precision and recall.
  1. Deployment

The recommendation system is executed by us in an application or healthcare tool. Make confirm that the system offers explanations for their suggestions, as clarification is much important in healthcare.

  1. Post-Deployment Monitoring

Usually, update the model with the result of new patient and validate regularly of our suggestions with healthcare professionals.


  • Data Sensitivity: The data of the patient must highly confidential. We establish that every data compiles with regulations such as Health Insurance Portability and Accountability Act (HIPAA).
  • Complex Interactions: The critical interactions includes, drug interactions binds with individual genetic variations is getting complicated to our model.
  • Bias: This system does not maintain the biases that presents in training data and it results in hazardous or suboptimal recommendations.

Extensions/Advanced Approaches

  • Genetics-Based Recommendations: We make use of pharmacogenomics data to design recommendations based on their genetic profiles of an individual.
  • Real-time Monitoring: It combines with Electronic Health Records (EHR) for getting updates in real-time data and recommendations.
  • Explainable AI (XAI): The model explores its decision is validating by us that is very important for experts in health care to trust and understand our recommendations.

Special note: This drug recommendation system never replaces the judgment of medical professionals. It acts as a tool to support decision making and offers recommendations for us or the professional’s ideas. Discussing or interacting with healthcare experts during the system design, development and deployment is very essential to attain our projects.

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Drug Recommendation System Using Machine Learning Thesis Topics

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Drug Recommendation System using Machine Learning Topics
  1. An Intelligent Disease Prediction and Drug Recommendation Prototype by Using Multiple Approaches of Machine Learning Algorithms


Data mining, drug recommendation system, NLP, sentiment analysis

            Our article provides medical suggestions to the customers based on ratings and health status. We employed various prototypes for disease forecasting. We examined the reviews by using Vader tool and sentiment analysis based on NLP. To suggest the medicines, we used probabilistic and weighted average techniques. At last, we conclude that, our system assist various healthcare applications and can be used in future researches.

  1. Archimedes Optimization with Enhanced Deep Learning based Recommendation System for Drug Supply Chain Management


Drug reviews, Supply chain management, Drug recommendation, Deep learning, Optimization algorithm

            A drug suggestion framework is proposed for drug supply chain management in our study named Archimedes Optimization with Enhanced Deep Learning based Recommendation System (AOAEDL-RS). We preprocessed the data by using AOAEDL-RS method. Then we performed drug review categorization by utilizing CBLSTM-CNN approach. Finally, for accurate hyperparameter adjustment of CBLSTM-CNN, AOAEDL-RS method utilized AOA.

  1. DKFM: Dual Knowledge-Guided Fusion Model for Drug Recommendation


Electronic health record, Healthcare, Molecular knowledge

            Our study suggested a drug recommendation system denoted DKFM with molecular knowledge-guided dual-level drug fusion technique. We can evaluate the effect of patient’s current status by utilizing DKFM that combines the patient’s diagnosis and procedure essential data. To acquire patient’s drug sets, we input the diagnosis and procedure data into drug functional group encoder. At last, we selected the optimal medicine for patient’s current health status.

  1. Deep-Learning-Based Drug Recommendation and ADR Detection Healthcare Model on Social Media


Adverse drug reaction (ADR) classification, collaborative filtering, recommendation system, social media, support vector machine

            A review of medical recommendation framework and ADR research issues are proposed in our paper. We investigated ADR classifications with various methods such as SVM, NB, LR, RF, and DNN. We discussed about the data preprocessing and feature extraction techniques. For language diversity and clinical characteristics, we examined the preprocessed data. We performed multilayer content and collaborative filtering for similarity indexing. For ADR identification and categorization, DNN offers greater end results.

  1. Radial Subset Clustering Feature-Based Deep Spectral Neural Classification for relational drug recommendation


Drug compound analysis, pattern prediction, subset clustering, feature selection

            To forecast the relational drug suggestion, a relative drug compound analysis based on RSCF-DSNC is presented in our research. Then we preprocessed the data and marginalized the labeled data to calculate Relative Drug Intensive Weight. Drug molecules closeness is forecasted by RSCFS. We detected the drug molecule’s relational class by trained the chosen features into DSNC integrated with CNN. Therefore, our drug suggestion model provides better results.

  1. Drug Recommendations Using a “Reviews and Sentiment Analysis” by a Recurrent Neural Network


Recurrent neural network (RNN), natural language processing (NLP)

            A drug suggestion model is recommended in our study to forecast the appropriate medicine by considering reviews of patients. We examined the efficiency of the model in terms of various metrics. We collected relevant data from patient’s information by employing Natural Language Processing method. We evaluated the textual data by using an ML based method named RNN. As a consequence, our suggested model efficiently suggests drugs for patients.

  1. MGEDR: A Molecular Graph Encoder for Drug Recommendation


Molecular graph encoder, Graph neural network

            A molecular graph encoder for drug recommendation that is denoted by MGEDR is suggested in our study to record the patient’s health condition. To acquire the precise drug description, we encode the drug molecular graph and functional groups independently. We efficiently captured the molecule’s relevant features by creating the degree encoder and functional groups encoder. As a result, our recommended framework offers greater outcomes.

  1. Adaptive Multi-Hop Deep Learning based Drug Recommendation System with Selective Coverage Mechanism


Neural Memory Network, Attention Mechanism, Coverage Mechanism, Adaptive Multi-Hop Reading

            A selective coverage mechanism and adaptive memory neural network is suggested in our article and our drug suggestion system is integrated with reading. We carried out data filtering and attention weight tuning in iterative reading procedure by utilizing coverage mechanism and stored the temporal pattern encoding findings with patient’s health status by employing neural memory network. We retrieved important features and provide efficient drug suggestions.

  1. A Drug Recommendation System for Multi-disease in Health Care Using Machine Learning


Drug, Machine learning, Prescription

            Our system aimed to offer efficient drug recommendation for the sick persons suffering from several diseases by employing ML techniques. We utilized various supervised ML methods including Support Vector Machine (SVM), Random Forest, Decision Tree, and K-nearest neighbors to provide medicine suggestions. We carried out a comparative analysis to find out the optimal method. Results show that, Random Forest achieved highest performance.

  1. Drug Recommendation System based on Sentiment Analysis of Drug Reviews using Machine Learning


Recommender System, Smote, Bow, TF-IDF, Word2Vec

            A main objective of our study is to develop a drug suggestion framework. We utilized several vectorization processes such as Bow, TF-IDF, Word2Vec, and Manual Feature Analysis to forecast the sentiment through patient’s reviews and assist to suggest an appropriate medicine for a particular disease through the employment of various categorization techniques. We conclude that, LinearSVC method with TF-IDF vectorization provides better end results.


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