Naive Algorithm Research Articles

Sentiment Analysis of Hate Speech against DPR-RI on Twitter Using Naive Bayes and KNN Algorithms

According to surveys, the rise in social media users has resulted in an increase of hate speech, and Twitter is one of the most popular platforms for this type of speech. The tweet feature on Twitter enables users to make repeated instances of hate speech, making Twitter data very intriguing to analyze. This study aims to investigate whether a tweet contains hate speech towards the Indonesian House of Representatives (DPR-RI). The research employed crawling techniques to gather data from Twitter using the Twitter API feature. The Naïve Bayes algorithm was applied, and the results were compared with the accuracy of the K-Nearest Neighbor. After preprocessing, the total data obtained was 1,494, with 956 test data and 538 training data. The study revealed that Twitter users' sentiment towards DPR-RI was 49.2% positive and 50.8% negative sentiment when tested using Naïve Bayes. Meanwhile, KNN showed 23.4% positive and 76.6% negative sentiment. The high negative sentiment in both classifiers suggests that Twitter users frequently express hate speech towards DPR-RI. Naïve Bayes algorithm showed the highest prediction accuracy at 98.32%, while the K-Nearest Neighbor algorithm had an accuracy of only 62.84%.

Start Time Planning for Cyclic Queuing and Forwarding in Time-Sensitive Networks

Time-sensitive networking (TSN) is a kind of network communication technology applied in fields such as industrial internet and intelligent transportation, capable of meeting the application requirements for precise time synchronization and low-latency deterministic forwarding. In TSN, cyclic queuing and forwarding (CQF) is a traffic shaping mechanism that has been extensively discussed in the recent literature, which allows the delay of time-triggered (TT) flow to be definite and easily calculable. In this paper, two algorithms are designed to tackle the start time planning issue with the CQF mechanism, namely the flow–path–offset joint scheduling (FPOJS) algorithm and congestion-aware scheduling algorithm, to improve the scheduling success ratio of TT flows. The FPOJS algorithm, which adopts a novel scheduling object—a combination of flow, path, and offset—implements scheduling in descending order of a well-designed priority that considers the resource capacity and resource requirements of ports. The congestion-aware scheduling algorithm identifies and optimizes congested ports during scheduling and substantially improves the scheduling success ratio by dynamically configuring port resources. The experimental results demonstrate that the FPOJS algorithm achieves a 39% improvement in the scheduling success ratio over the naive algorithm, 13% over the Tabu-ITP algorithm, and 10% over the MSS algorithm. Moreover, the algorithm exhibits a higher scheduling success ratio under large-scale TSN.

Classifying Digital Medical Images for Breast Cancer Prediction Using Machine Learning

Statistics show that among the 1.67 million cancer reported cases worldwide, breast cancer is the most common cancer among women and constitutes the largest burden of the disease in developing countries. However, if detected early enough, it can be managed. Mammography is one of the best ways to identify and diagnose breast abnormalities among various medical imaging modalities. It typically detects signs and symptoms of breast cancer, including microcalcifications, lumps, nodules, architectural abnormalities, asymmetry, bilateral asymmetry, etc. These features can be benign or cancerous when they appear in the breast. Researchers have focused on creating fully automated computer-aided design methods to help radiologists combat this type of cancer. Artificial Intelligence( AI) -based algorithms have been essential in creating systems that allow for automated diagnosis, rapid response, and low mortality. In this work, several machine learning methods were compared—such as logistic regression, naive Bayesian Gaussian algorithms, support vector machines (SVM), linear support vector machines (SVM), and artificial neural networks (ANN). Processing time and accuracy were the main evaluation metrics where naive Bayes outperformed SVM, followed by linear SVM and logistic regression, with ANNs failing in accuracy. These results highlight how naive Bayes algorithms can help in early detection of breast cancer, leading to faster and more efficient treatments and ultimately better patient care.

Weighted naïve bayes multi-user classification for adaptive authentication

Abstract Machine learning classification algorithms have been extensively utilized in addressing user authentication challenges. Nonetheless, a majority of solutions categorize users into three classes, whereas adaptive authentication scenarios necessitate classification beyond this threshold. The rationale behind this limitation has not been thoroughly explored. The current study leveraged the Naive Bayes theorem for user authentication endeavors to assess the risk associated with login attempts. The Naive Bayes Machine Learning algorithm, along with its variations such as Gaussian, Categorical, and Bernoulli, was applied on both weighted and unweighted datasets to ascertain risk levels and categorize them into six classes. Additionally, the classification task was executed using alternative algorithms. The outcomes of cross-validation and comparative analyses revealed that the performance was commendable for up to three classes, after which a decrease was observed in certain Naive Bayes and SVM classifiers. Among the Naïve Bayes family, the Bernoulli NB algorithm exhibited superior performance but was surpassed by Decision Trees, SVM, XGB, and Random Forests. Notably, the weighted dataset consistently outperformed the unweighted counterpart, with the allocation of weights significantly influencing algorithmic efficacy. The 80:20 split strategy yielded the most favorable outcomes in contrast to the 70:30 and 60:40 splits, albeit no significant variances were detected during cross-validation. Non-Naïve Bayes algorithms demonstrated superior performance compared to Naïve Bayes algorithms. For Naïve Bayes, optimal performance is achieved with three classes, highlighting its utility in conditional risk calculation, while non-Naïve Bayes multi-classification algorithms are more suitable for classification tasks due to the problem’s inherent compatibility with conditional probabilities. In conclusion, it is imperative to acknowledge that the characteristics of the data, the use of weights, and the data splitting methodology significantly influence the accuracy of machine learning algorithms in multi-class user classification.

HF skywave communication for air-to-ground networks: A cooperative transmission framework

High-Frequency (HF) communication is widely used for long-distance transmission in remote and disaster areas. However, the dynamic nature of the ionosphere and multipath propagation in the HF channel pose significant challenges to designing efficient and robust communication systems. In this paper, we propose a Fixed Station (FS) and frequency matching method, as well as a power allocation method, to improve the sum-rate of air-to-ground HF communication networks. We derive optimal power allocation among users that share the same frequency, based on which a modified water-filling algorithm is used to solve the power allocation problem in multi-user scenarios, while a low-complexity algorithm is proposed to solve the integer optimization problem of frequency-FS matching. Simulation results demonstrate that the proposed algorithm outperforms the naive algorithm, indicating its effectiveness.

Sentiment Analysis of Indonesia 2024 Election with a Comparison of Naive Bayes and KNN Algorithms on Twitter

In the 2024 General Election, the Recapitulation Information System (SIREKAP) was used to capture the vote count results electronically. However, the use of SIREKAP raises various opinions in the community, both positive and negative, regarding the accuracy of the uploaded data. This study aims to analyze public sentiment towards the use of SIREKAP in the 2024 Election through Twitter data, using the Naive Bayes and KNN algorithms. The results showed that the Naive Bayes algorithm was superior with an accuracy of 93.37%, while KNN achieved an accuracy of 77.92%. The novelty of this research is to conduct sentiment analysis and provide insight into how people perceive the use of SIREKAP in the 2024 Election through Twitter data.

Accelerometers-based position and time interval comparisons for predicting the behaviors of young bulls housed in a feedlot system

Animal behavior monitoring is an important tool for animal production. This behavior monitoring strategy can indicate the well-being and health of animals, which can lead to better productive performance. This study aimed to assess the most effective accelerometer attachment position (on either the halter or a neck collar) and data transmission time intervals (ranging from 6 to 600 s) for predicting behavioral patterns, including water and food intake frequencies, as well as other activities in young beef cattle bulls within a feedlot system. A range of machine learning algorithms were applied to satisfy the aims of the study, including the random forest, support vector machine, multilayer perceptron, and naive Bayes classifier algorithms. All studied models produced high performance metrics (above 0.90) when using both attachment positions, except for the models built using the naive Bayes classifier. Therefore, coupling accelerometers with collars is a more viable alternative for use on animals, as doing so is easier than applying accelerometers to halters. Utilizing a dataset with more observations (i.e., shorter time intervals) did not result in considerable improvements in the performance metrics of the trained models. Therefore, using datasets with fewer observations is more advantageous, as it can lead to decreased computational and temporal demands for model training, in addition to saving the battery of the device considered in this study.

TF-IDF combined rank factor Naive Bayesian algorithm for intelligent language classification recommendation systems

With the continuous improvement of smart language systems, a large amount of language text data has emerged. How to efficiently and accurately process these text data has become an important challenge. Therefore, a speech classification recommendation system based on improved Naive Bayesian algorithm is proposed. The system first adopts the traditional Bayesian algorithm to classify language texts. The Term Frequency-Inverse Document Frequency and rank factor are combined to increase the weight of feature languages. Then, the classified language texts are combined with the improved algorithm for language classification recommendation. Finally, performance testing and simulation applications are conducted on the system. From the results, in the Gutenberg corpus, the research algorithm had the highest accuracy and completeness, with 98.5 % and 91.6 %, respectively, and the lowest values were 92.6 % and 89.4 %. The average values were 95.5 % and 91.1 %, with an F1 value of about 92.6 %. In the Brown corpus, the average accuracy, completeness, and F1 value of the designed algorithm were 96.2 %, 91.2 %, and 93.2 %, respectively. When the number of online customers reached 1000, the response time of the designed Chinese system was 1.15 s, the classification recommendation accuracy was 95 %, and the system stability was about 83 % on average. The response time of the English system was 0.64 s, the classification recommendation accuracy was 96 %, and the system stability was about 90 % on average. It shows that the designed method can significantly enhance the operation accuracy of the classification recommendation system.

PERBANDINGAN ANALISIS REGRESI LOGISTIK BINER DAN NAÏVE BAYES CLASSIFIER UNTUK MEMPREDIKSI FAKTOR RESIKO DIABETES

Diabetes is a global health problem that is increasing in prevalence worldwide. This study compares the performance of two data analysis methods, namely binary logistic regression and naïve bayes classifier in predicting diabetes risk. This study aims to identify factors that significantly affect diabetes risk and classify diabetes risk using binary logistic regression, then compare the classification with the naive bayes classifier algorithm. Binary logistic regression models the relationship between independent predictor variables and binary dependent variables, while naïve bayes classifier uses the assumption of independence between variables. In this study, both methods were evaluated based on accuracy, sensitivity, specificity and positive predictive value. The results show that the factors that influence the risk of diabetes are Age, Gender, Polyuria, Polydipsia, Genital thrush, Itching, Irritability, and Partial paresis. Furthermore, the binary logistic regression results have a higher classification accuracy (92.31%) compared to the naïve bayes classifier (84.61%). Therefore, binary logistic regression was identified as the best method to predict diabetes risk in the context of this study

The Symplectic Adjoint Method: Memory-Efficient Backpropagation of Neural-Network-Based Differential Equations.

The combination of neural networks and numerical integration can provide highly accurate models of continuous-time dynamical systems and probabilistic distributions. However, if a neural network is used n times during numerical integration, the whole computation graph can be considered as a network n times deeper than the original. The backpropagation algorithm consumes memory in proportion to the number of uses times of the network size, causing practical difficulties. This is true even if a checkpointing scheme divides the computation graph into subgraphs. Alternatively, the adjoint method obtains a gradient by a numerical integration backward in time; although this method consumes memory only for single-network use, the computational cost of suppressing numerical errors is high. The symplectic adjoint method proposed in this study, an adjoint method solved by a symplectic integrator, obtains the exact gradient (up to rounding error) with memory proportional to the number of uses plus the network size. The theoretical analysis shows that it consumes much less memory than the naive backpropagation algorithm and checkpointing schemes. The experiments verify the theory, and they also demonstrate that the symplectic adjoint method is faster than the adjoint method and is more robust to rounding errors.

Secure Range-Searching Using Copy-And-Recurse

Range searching is the problem of preprocessing a set of points P, such that given a query range gamma we can efficiently compute some function f(P cap gamma). For example, in a 1 dimensional range counting query, P is a set of numbers, gamma is a segment and we need to count how many numbers of P are in gamma. In higher dimensions, P is a set of d dimensional points and the query range is some volume in R^d. In general, we want to compute more than just counting, for example, the average of P cap gamma. Range searching has applications in databases where some SELECT queries can be translated to range queries. It had received a lot of attention in computational geometry where a data structure called partition tree was shown to solve range queries in time sub-linear in |P| using space only linear in |P|. In this paper we consider partition trees under FHE where we answer range queries without learning the value of the points or the parameters of the range. We show how partition trees can be securely traversed with O(t n^{1-1/d+epsilon} + n^{1+epsilon}) operations, where n=|P|, t is the number of operations needed to compare to gamma and epsilon>0 is a parameter. When the ranges are axis-parallel hyper-boxes the running time is O(t n^epsilon + n log^{d-1} n). As far as we know, this is the first non-trivial bound on range searching under FHE and it improves over the naive solution that needs O(t n) operations. Our algorithms are independent of the encryption scheme but as an example we implemented them using the CKKS FHE scheme. Our experiments show that for databases of sizes 2^{23} and 2^{25}, our algorithms run x2.8 and x4.7 (respectively) faster than the naive algorithm. The improvement of our algorithm comes from a method we call copy-and-recurse. With it we efficiently traverse a r-ary tree (where each inner node has r children) that also has the property that at most xi of them need to be recursed into when traversing the tree. We believe this method is interesting in its own and can be used to improve traversals in other tree-like structures.

Research on E-learning interactive English vocabulary recommendation education system based on naive Bayes algorithm

E-learning is an interactive online learning mode that can enhance students' interest in learning in an entertainment environment. This article aims to develop an English vocabulary learning recommendation system based on decision tree algorithm and naive Bayesian algorithm to provide personalized learning suggestions and help learners learn and remember words more effectively. Firstly, a large amount of English vocabulary data was collected, preprocessed, and feature extracted. Decision tree algorithms were selected as the foundation, and through analysis of existing data, a decision tree was generated to classify new data. Using learners' characteristics (such as age, learning objectives, etc.) and learning history (such as learned words, learning time, etc.) as inputs, a personalized recommendation model was constructed, This model can recommend suitable learning content for learners based on their personalized needs and learning situation. In order to better understand learners' learning progress, a naive Bayesian model was trained using learners' learning history and progress information to analyze their current learning progress and predict their future learning situation. Through testing and evaluation of actual learners, the recommendation system performs well in providing personalized learning suggestions, and has a significant improvement effect on learners' vocabulary learning.

Machine Learning Based Spam Comments Detection on Youtube: A Comparison of Regression Models

The rise of spam comments on platforms like YouTube has become a significant concern, as they not only hinder genuine user engagement but also pose serious risks to users' safety and privacy. Machine Learning (ML) offers a powerful solution to combat spam comments by automating the process of detecting and preventing them. With the ability to analyze vast amounts of data and patterns, ML algorithms can effectively distinguish between legitimate comments and those that are spam. One of the commonly employed approaches in ML for spam comment detection is the Naive Bayes classification algorithm. Naive Bayes is a probabilistic algorithm that calculates the likelihood of a comment being spam based on its characteristics and the occurrence of specific keywords or phrases that are typical of spam content. By training the algorithm on a labeled dataset of spam and non-spam comments, it can learn to recognize patterns and generalize its understanding to new, unseen comments. Achieving a detection accuracy of 92.78% is indeed promising, but researchers and developers continue to explore other ML techniques and combinations to further improve the accuracy and robustness of spam comment detection systems. Ensemble methods, deep learning, and Natural Language Processing (NLP) techniques are among the advanced approaches gaining attention in this domain. One crucial aspect of an effective spam detection system is its adaptability and responsiveness to emerging spam tactics.

Perbandingan Algoritma Naive Bayes Berbasis Feature Selection Gain Ratio dengan Naive Bayes Kovensional dalam Prediksi Komplikasi Hipertensi

High blood pressure is a significant public health problem with a high prevalence in the Indo-nesian population. 2018 Riset Kesehatan Dasar (Riskesdas) data shows a prevalence of hyperten-sion of 34.1% in individuals aged 18 years and over, with the highest figure in South Kalimantan and the lowest in Papua. Complications arising from hypertension can have serious impacts on organs such as the brain, eyes, heart and kidneys. The Naive Bayes algorithm is generally used in disease prediction, but the Naive Bayes algorithm has problems when selecting attributes, because Naive Bayes itself is a statistical classification method that is only based on Bayes' Theorem so it can only be used with the aim of predicting the probability of membership in a group or class. So attribute weighting is needed to increase accuracy more effectively. This research introduces Gain Ratio as an attribute weighting method to increase the accuracy of Naive Bayes. The aim of this study was to compare conventional Naive Bayes with Naive Bayes Gain Ratio in predicting complications of high blood pressure. The research results show that feature selection with gain ratio weighting can increase the accuracy of naive Bayes classification, with an average increase in accuracy of 20% compared to naive Bayes without feature selection. The precision value increased by 21% in the naive Bayes gain ratio algorithm for the kidney failure class, an increase of 3% in the heart class, and an increase of 31% in the stroke class, for the recall value the naive Bayes gain ratio increased by 35% in the heart class while in the kidney failure and stroke classes did not increase the recall value.

A case study on the relationship between risk assessment of scientific research projects and related factors under the Naive Bayesian algorithm

This paper delves into the nuanced dynamics influencing the outcomes of risk assessment (RA) in scientific research projects (SRPs), employing the Naive Bayes algorithm. The methodology involves the selection of diverse SRPs cases, gathering data encompassing project scale, budget investment, team experience, and other pertinent factors. The paper advances the application of the Naive Bayes algorithm by introducing enhancements, specifically integrating the Tree-augmented Naive Bayes (TANB) model. This augmentation serves to estimate risk probabilities for different research projects, shedding light on the intricate interplay and contributions of various factors to the RA process. The findings underscore the efficacy of the TANB algorithm, demonstrating commendable accuracy (average accuracy 89.2%) in RA for SRPs. Notably, budget investment (regression coefficient: 0.68, P < 0.05) and team experience (regression coefficient: 0.51, P < 0.05) emerge as significant determinants obviously influencing RA outcomes. Conversely, the impact of project size (regression coefficient: 0.31, P < 0.05) is relatively modest. This paper furnishes a concrete reference framework for project managers, facilitating informed decision-making in SRPs. By comprehensively analyzing the influence of various factors on RA, the paper not only contributes empirical insights to project decision-making but also elucidates the intricate relationships between different factors. The research advocates for heightened attention to budget investment and team experience when formulating risk management strategies. This strategic focus is posited to enhance the precision of RAs and the scientific foundation of decision-making processes.

On the Practical Power of Automata in Pattern Matching

Many papers in the intersection of theoretical and applied algorithms show that the simple, asymptotically less efficient algorithm, performs better than the bestcomplex theoretical algorithms on random data or in specialized “real world” applications. This paper considers the Knuth–Morris–Pratt automaton, and shows a counter-intuitive practical result. The classical pattern matching paradigm is that of seeking occurrences of one string—the pattern, in another—the text, where both strings are drawn from an alphabet set Σ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Sigma$$\\end{document}. Assuming the text length is n and the pattern length is m, this problem can naively be solved in time O(nm). In Knuth, Morris and Pratt’s seminal paper of 1977, an automaton, was developed that allows solving this problem in time O(n) for any alphabet. This automaton, which we will refer to as the KMP-automaton, has proven useful in solving many other problems. A notable example is the parameterized pattern matching model. In this model, a consistent renaming of symbols from Σ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Sigma$$\\end{document} is allowed in a match. The parameterized matching paradigm has proven useful in problems in software engineering, computer vision, and other applications. It has long been believed that for texts where the symbols are uniformly random, the naive algorithm will perform as well as the KMP algorithm. In this paper, we examine the practical efficiency of the KMP algorithm versus the naive algorithm on a randomly generated text. We analyze the time under various parameters, such as alphabet size, pattern length, and the distribution of pattern occurrences in the text. We do this for both the original exact matching problem and parameterized matching. While the folklore wisdom is vindicated by these findings for the exact matching case, surprisingly, the KMP algorithm always works significantly faster than the naive in the parameterized matching case. We check this hypothesis for DNA texts and image data and observe a similar behavior as in the random text. We also show a very structured exact matching case where the automaton is much more efficient.

POI Route Recommendation Model Based on Symmetrical Naive Bayes Classification Spatial Accessibility and Improved Cockroach Swarm Optimization Algorithm

The commonly used POI route recommendation methods usually ignore the effects of tourists’ interests and transportation geographical conditions, and so may not output the optimal results. To solve the problems, we propose a POI route recommendation model based on symmetrical Naive Bayes classification spatial accessibility (NBCSA) and an improved cockroach swarm optimization algorithm (ICSOA), aiming to recommend POI routes that satisfy tourists’ interests and have the lowest travel costs under tourism transportation geographical conditions. Using the historical POIs visited by tourists as the training set, we construct an improved symmetrical Naive Bayes classification algorithm (NBCA), and the POIs in the destination city are divided into categories by tourists’ preferences. Then we propose an improved NBCSA model to calculate the spatial accessibility field strength (SAFS) for each category’s POIs. Based on the recommended POIs, we propose the ICSOA to recommend optimal POI routes. The experiment verifies that the proposed algorithm can effectively classify the POIs and recommend POIs that best match the tourists’ interests and produce the lowest travel costs. Compared with the TCA and GDA method, the proposed algorithm can output the POI routes with lower travel costs and has higher algorithm execution efficiency. Among the output optimal routes, the proposed algorithm can reduce costs by 5.62% at the lowest and 52.25% at the highest.

Mixed-Effects Contextual Bandits

We study a novel variant of a contextual bandit problem with multi-dimensional reward feedback formulated as a mixed-effects model, where the correlations between multiple feedback are induced by sharing stochastic coefficients called random effects. We propose a novel algorithm, Mixed-Effects Contextual UCB (ME-CUCB), achieving tildeO(d sqrt(mT)) regret bound after T rounds where d is the dimension of contexts and m is the dimension of outcomes, with either known or unknown covariance structure. This is a tighter regret bound than that of the naive canonical linear bandit algorithm ignoring the correlations among rewards. We prove a lower bound of Omega(d sqrt(mT)) matching the upper bound up to logarithmic factors. To our knowledge, this is the first work providing a regret analysis for mixed-effects models and algorithms involving weighted least-squares estimators. Our theoretical analysis faces a significant technical challenge in that the error terms do not constitute martingales since the weights depend on the rewards. We overcome this challenge by using covering numbers, of theoretical interest in its own right. We provide numerical experiments demonstrating the advantage of our proposed algorithm, supporting the theoretical claims.

Naïve Bayes Algorithm for Sentiment Analysis of Blibli.com Review on Google Play Store

Currently, there are approximately 354 million active mobile phones in Indonesia, placing the country fourth globally in terms of the highest number of mobile phone users. E-Commerce, as a form of online transaction, enables the digital exchange of goods and services to meet daily needs. This research aims to implement sentiment analysis using the Naive Bayes classification algorithm as a method to gather user opinions. Thus, the study not only provides insights into customer satisfaction with Blibli.com but also serves as a basis for potential improvements in services or feature development to enhance the online shopping experience. Overall, the Naive Bayes algorithm successfully achieved an accuracy of around 84%, demonstrating its proficiency in categorizing sentiment in reviews. When focusing on negative data, the Naive Bayes algorithm exhibited a precision of approximately 79%, recall of around 95%, and an f1-score of about 86%, indicating its success in identifying and classifying negative reviews with high precision and sensitivity. On the positive side, the Naive Bayes algorithm achieved a precision of about 91%, recall of around 83%, and an f1-score of about 87%.

Abstract 5145: Association of a ctDNA biomarker of treatment response with clinical outcomes in a real-world pan-cancer cohort treated with tyrosine kinase inhibitors

Abstract Clinical evidence suggests that early changes in circulating tumor DNA tumor fraction (ctDNA TF) is predictive of patient response to immune checkpoint inhibitors, but the extent to which this approach can be used for patients on Tyrosine Kinase Inhibitors (TKIs) is not known. Here, we use xF Monitor to estimate ctDNA TF in patients that received TKIs and show that changes in circulating TF correlate with outcomes in a real-world pan-cancer cohort. Tempus xF Monitor is a ctDNA assay that measures quantitative molecular changes in ctDNA TF by utilizing diverse genomic events, dynamically weighting somatic variant allele frequencies and copy number variants, while using germline information to inform these estimates and providing single nucleotide variant (SNV) results on 105 genes. Molecular responders (MR) were defined as patients with ≥50% reduction in ctDNA TF between baseline and on-treatment time points, consistent with our previously established threshold. Deidentified patient records from the Tempus multimodal database were analyzed if patients had an xF test ≤ 15 weeks prior to the start of their first TKI regimen and an xF test 15 -180 days post-TKI initiation. Patients received TKI agents as classified in the Tempus Medical Ontology, which assigns a unique ID to key clinical concepts, validated and harmonized across industry standard terminologies. The TKI list was further validated using the NCI Metathesaurus (https://ncim.nci.nih.gov/ncimbrowser/) TKI concept (NCI Code C1967). Clinical endpoints were defined from TKI start to the first progression event or death (rwPFS) or death (rwOS), both censored on the last follow-up in event-free patients. Kaplan Meier analysis and Cox proportional hazard models were fitted to compare MR status and survival outcomes on TKI. The evaluable pan-cancer cohort N=69, median age = 61.5 yo, advanced/metastatic disease = 72%, 65% were female, consisted of &amp;gt; 10 cancer diagnoses (35% NSCLC, 26% BC), 41% 1L. In this cohort, 49% of patients received a TKI combination regimen, 21% with CT, 18% with ICI. 68% of patients with NGS testing received matched targeted mutation therapies based on NCCN guidelines. Patients classified as MR based on xF Monitor results had improved survival outcomes with a median rwOS of 951 days (vs 439 days for non-MRs). MR patients had significantly longer rwPFS (HR = 0.383, p = 0.005, CI = 0.12 - 0.75) and rwOS (HR = 0.205, p = 0.004, CI = 0.07 - 0.61) than non-MRs. xF Monitor is a novel tumor naive serial quantitative ctDNA TF algorithm off the Tempus xF assay that has the potential to be used clinically as a monitoring biomarker to stratify patients who are likely to benefit from TKI therapy. xF monitor can be used as a strategy to identify patients at a high risk of progression who could benefit from early switch or intensifying therapy. Results need to be prospectively validated in a larger cohort. Citation Format: Akash Mitra, John Guittar, Regina Schwind, Terri Driessen, Matthew Berginski, Christine Lo, Michelle M. Stein, Jonathan Freaney, Seung Won Hyun, Chithra Sangli, Kate Sasser, Halla Nimeiri, Aadel Chaudhuri. Association of a ctDNA biomarker of treatment response with clinical outcomes in a real-world pan-cancer cohort treated with tyrosine kinase inhibitors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5145.